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EECS 498-007/598-005 Assignment 6-1: Variational AutoEncoders\n","\n","Before we start, please put your name and UMID in following format\n","\n",": Firstname LASTNAME, #00000000   //   e.g.) Justin JOHNSON, #12345678"]},{"cell_type":"markdown","metadata":{"id":"ZsZYgVWdALCH"},"source":["**Your Answer:**   \n","Hello WORLD, #XXXXXXXX"]},{"cell_type":"markdown","metadata":{"id":"VJZ8AefthL95"},"source":["\n","# Variational Autoencoder\n","\n","In this notebook, you will implement a variational autoencoder and a conditional variational autoencoder with slightly different architectures and apply them to the popular MNIST handwritten dataset. Recall from lecture (https://web.eecs.umich.edu/~justincj/slides/eecs498/FA2020/598_FA2020_lecture19.pdf), an autoencoder seeks to learn a latent representation of our training images by using unlabeled data and learning to reconstruct its inputs. The *variational autoencoder* extends this model by adding a probabilistic spin to the encoder and decoder, allowing us to sample from the learned distribution of the latent space to generate new images at inference time."]},{"cell_type":"markdown","metadata":{"id":"JtA1_PsYhs24"},"source":["## Setup Code\n","Before getting started, we need to run some boilerplate code to set up our environment, same as previous assignments. You'll need to rerun this setup code each time you start the notebook.\n","\n","First, run this cell load the autoreload extension. This allows us to edit .py source files, and re-import them into the notebook for a seamless editing and debugging experience.\n"]},{"cell_type":"code","metadata":{"id":"OKXSEQjRh63r","executionInfo":{"status":"ok","timestamp":1611293537267,"user_tz":480,"elapsed":485,"user":{"displayName":"김진혁","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh-KNOJTlJhKZYC2IjbUIPFTFKWoCXbFj273Tuhrg=s64","userId":"18139102342976554490"}}},"source":["%load_ext autoreload\n","%autoreload 2"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"eIXWSou6h_S6"},"source":["### Google Colab Setup\n","Next we need to run a few commands to set up our environment on Google Colab. If you are running this notebook on a local machine you can skip this section.\n","\n","Run the following cell to mount your Google Drive. Follow the link, sign in to your Google account (the same account you used to store this notebook!) and copy the authorization code into the text box that appears below."]},{"cell_type":"code","metadata":{"id":"evqEGDXRipC-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1611293620369,"user_tz":480,"elapsed":83580,"user":{"displayName":"김진혁","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh-KNOJTlJhKZYC2IjbUIPFTFKWoCXbFj273Tuhrg=s64","userId":"18139102342976554490"}},"outputId":"b2e1dacb-eb7a-4208-8d22-670ffba1a4de"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2y_JbZTlDoai"},"source":["Now recall the path in your Google Drive where you uploaded this notebook, fill it in below. If everything is working correctly then running the folowing cell should print the filenames from the assignment:\n","\n","```\n","['eecs598', 'gan.py', 'generative_adversarial_networks.ipynb', 'a6_helper.py', 'vae.py', 'variational_autoencoders.ipynb']\n","```"]},{"cell_type":"code","metadata":{"id":"Z8OU2pRkivyc","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1611293644600,"user_tz":480,"elapsed":526,"user":{"displayName":"김진혁","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh-KNOJTlJhKZYC2IjbUIPFTFKWoCXbFj273Tuhrg=s64","userId":"18139102342976554490"}},"outputId":"81387bcb-59ea-4b42-efa4-f75050890dc5"},"source":["import os\n","\n","# TODO: Fill in the Google Drive path where you uploaded the assignment\n","# Example: If you create a 2020FA folder and put all the files under A6 folder, then '2020FA/A6'\n","GOOGLE_DRIVE_PATH_AFTER_MYDRIVE = 'EECS498-007/A6'\n","GOOGLE_DRIVE_PATH = os.path.join('drive', 'My Drive', GOOGLE_DRIVE_PATH_AFTER_MYDRIVE)\n","print(os.listdir(GOOGLE_DRIVE_PATH))"],"execution_count":3,"outputs":[{"output_type":"stream","text":["['variational_autoencoders.ipynb', 'generative_adversarial_networks.ipynb', 'a6_helper.py', 'vae.py', 'gan.py', 'eecs598']\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"GJ7auXOMi4rw"},"source":["Once you have successfully mounted your Google Drive and located the path to \n","this assignment, run the following cell to allow us to import from the `.py` files of this assignment. If it works correctly, it should print the message:\n","\n","```\n","Hello from vae.py!\n","Hello from a6_helper.py!\n","```\n","\n","as well as the last edit time for the file `vae.py`."]},{"cell_type":"code","metadata":{"id":"5zOOPYIUjCUO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1611293651110,"user_tz":480,"elapsed":4604,"user":{"displayName":"김진혁","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh-KNOJTlJhKZYC2IjbUIPFTFKWoCXbFj273Tuhrg=s64","userId":"18139102342976554490"}},"outputId":"ae225d52-f489-4511-ce25-b610d33a398b"},"source":["import sys\n","sys.path.append(GOOGLE_DRIVE_PATH)\n","\n","import time, os\n","os.environ[\"TZ\"] = \"US/Eastern\"\n","time.tzset()\n","\n","from vae import hello_vae\n","hello_vae()\n","\n","from a6_helper import hello_helper\n","hello_helper()"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Hello from vae.py!\n","Hello from a6_helper.py!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"JuIiv2bhjFoC"},"source":["Load several useful packages that are used in this notebook:"]},{"cell_type":"code","metadata":{"id":"sLdT7GSljI0f","executionInfo":{"status":"ok","timestamp":1611293695652,"user_tz":480,"elapsed":2932,"user":{"displayName":"김진혁","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh-KNOJTlJhKZYC2IjbUIPFTFKWoCXbFj273Tuhrg=s64","userId":"18139102342976554490"}}},"source":["from eecs598.grad import rel_error\n","from eecs598.utils import reset_seed\n","import math\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","from torch.nn import init\n","import torchvision\n","import torchvision.transforms as T\n","import torch.optim as optim\n","from torch.utils.data import DataLoader\n","from torch.utils.data import sampler\n","import torchvision.datasets as dset\n","\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","\n","\n","# for plotting\n","plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n","plt.rcParams['font.size'] = 16\n","plt.rcParams['image.interpolation'] = 'nearest'\n","plt.rcParams['image.cmap'] = 'gray'"],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"_nqWhiLojS8M"},"source":["We will use GPUs to accelerate our computation in this notebook. Run the following to make sure GPUs are enabled:"]},{"cell_type":"code","metadata":{"id":"RdQhVgi5jVQp","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1611293698871,"user_tz":480,"elapsed":297,"user":{"displayName":"김진혁","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh-KNOJTlJhKZYC2IjbUIPFTFKWoCXbFj273Tuhrg=s64","userId":"18139102342976554490"}},"outputId":"c59a5b49-0adf-4f40-963f-f68f003fdedc"},"source":["if torch.cuda.is_available():\n","  print('Good to go!')\n","else:\n","  print('Please set GPU via Edit -> Notebook Settings.')"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Good to go!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"bcqRQILRjchz"},"source":["## Load MNIST Dataset\n","\n","\n","VAEs (and GANs as you'll see in the next notebook) are notoriously finicky with hyperparameters, and also require many training epochs. In order to make this assignment approachable, we will be working on the MNIST dataset, which is 60,000 training and 10,000 test images. Each picture contains a centered image of white digit on black background (0 through 9). This was one of the first datasets used to train convolutional neural networks and it is fairly easy -- a standard CNN model can easily exceed 99% accuracy. \n","\n","To simplify our code here, we will use the PyTorch MNIST wrapper, which downloads and loads the MNIST dataset. See the [documentation](https://github.com/pytorch/vision/blob/master/torchvision/datasets/mnist.py) for more information about the interface. The default parameters will take 5,000 of the training examples and place them into a validation dataset. The data will be saved into a folder called `MNIST_data`. "]},{"cell_type":"code","metadata":{"id":"mExnwvTXjcF_","colab":{"base_uri":"https://localhost:8080/","height":410,"referenced_widgets":["4b4cb0676bca40c19d178c2738b32286","21a2e8cb79f1460094a917892159c41c","3ed71f422e1b4b45a4c9b7196dcf9866","f8a7b7cb6d7b472e86e981e5b47268ca","ed37ca806dae4f67afcae82a3c5cbce6","9d69a3f747864b7882dee47b3afce865","13d959664572446b934c440dd6d55ef7","a852b67edd564bc7a897dce86d33d5ea","4597e887dd4547118d42d30551f73c2c","7d4e2c97f2574f8f9039e195d586d134","5e98f750dbb54448b3c5963ccd926f1d","f59da8fbbb8647e79c370a0cea4459e1","a9855644c38842a1af7e9d2797873019","6b557b0d35924a018ca2b8bf01896265","6a6c5a5569364988a410489cc9710ba0","c02a656accb7472b8d797ba69d353c5d","4dd65242ee334d7fbb76f2169df60e85","e32916c956284d85976814337d3acb66","0abe6d5d22c64912850bea9c0e693e15","1447f88b5d8a4e3982066fe85ec7fd24","75e1fd79b6e4482498923da51e6002b8","248318e06ba54a10b135d06698630ab1","487efb8e5bb346489da7a787dcd511c1","a1850878dc0e482ab4764b2e3381cc25","e24a1262906c41dd8ae6c5feb33c1bdb","47f1c453a8214a72a77498530e26ea20","91a0646d65964aa594403ad676fd337d","b55fe2216b3f4e31a68e7d685f9e8ff0","6233a3b103e643cc93aae46c316554c2","6ee43bcd2efe48a7a01ef9f1219715c6","37e8bb377b9f4cd2824738b969ad9f04","e627ed53023841d3b5a86df2dbc98298"]},"executionInfo":{"status":"ok","timestamp":1611293703255,"user_tz":480,"elapsed":1871,"user":{"displayName":"김진혁","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh-KNOJTlJhKZYC2IjbUIPFTFKWoCXbFj273Tuhrg=s64","userId":"18139102342976554490"}},"outputId":"c91ba720-d9ec-44f2-ed49-91714df13938"},"source":["batch_size = 128\n","\n","mnist_train = dset.MNIST('./MNIST_data', train=True, download=True,\n","                           transform=T.ToTensor())\n","loader_train = DataLoader(mnist_train, batch_size=batch_size,\n","                          shuffle=True, drop_last=True, num_workers=2)\n"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./MNIST_data/MNIST/raw/train-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4b4cb0676bca40c19d178c2738b32286","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Extracting ./MNIST_data/MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST_data/MNIST/raw\n","Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./MNIST_data/MNIST/raw/train-labels-idx1-ubyte.gz\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4597e887dd4547118d42d30551f73c2c","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Extracting ./MNIST_data/MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST_data/MNIST/raw\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./MNIST_data/MNIST/raw/t10k-images-idx3-ubyte.gz\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4dd65242ee334d7fbb76f2169df60e85","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Extracting ./MNIST_data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST_data/MNIST/raw\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST_data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e24a1262906c41dd8ae6c5feb33c1bdb","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Extracting ./MNIST_data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST_data/MNIST/raw\n","Processing...\n","Done!\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:480: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n","  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"CwDmYBjdhTrM"},"source":["## Visualize dataset"]},{"cell_type":"markdown","metadata":{"id":"Q2X_21cTwsox"},"source":["It is always a good idea to look at examples from the dataset before working with it. Let's visualize the digits in the MNIST dataset. We have defined the function `show_images` in `a6_helper.py` that we call to visualize the images.\n"]},{"cell_type":"code","metadata":{"id":"3JMbbxMkwrYg","colab":{"base_uri":"https://localhost:8080/","height":607},"executionInfo":{"status":"ok","timestamp":1611293725500,"user_tz":480,"elapsed":3961,"user":{"displayName":"김진혁","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh-KNOJTlJhKZYC2IjbUIPFTFKWoCXbFj273Tuhrg=s64","userId":"18139102342976554490"}},"outputId":"2d4d62ba-8709-4101-b2a9-130f9df50b9f"},"source":["from a6_helper import show_images\n","\n","imgs = loader_train.__iter__().next()[0].view(batch_size, 784)\n","show_images(imgs)"],"execution_count":8,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAqoAAAJkCAYAAADZZwOuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydebiN5ffGPydJocjQQJKiJAqJCqWSSholaS6ifBVSylipaEBIk2jURCSZiiSlFFFoEiXNRTNFcn5/7N/9Pnu/Z+9z9jlnD+97Wp/r6to5e3qe/Y7Pvda6V05ubi6GYRiGYRiGETR2yPYADMMwDMMwDCMedqNqGIZhGIZhBBK7UTUMwzAMwzACid2oGoZhGIZhGIHEblQNwzAMwzCMQGI3qoZhGIZhGEYg2TG/J3NyckqUd1Vubm5O9L9tfuHC5hdubH7hxuYXbmx+4ea/Nr9oTFE1DMMwDMMwAondqBqGYRiGYRiBxG5UDcMwDMMwjEBiN6qGYRiGYRhGILEbVcMwDMMwDCOQ2I2qYRiGYRhGCqhevTrVq1fnpptu4q+//uKvv/7i448/5uOPP+awww7jsMMOy/YQQ0dObm5ih4OSbn9g8wsXmZpfmzZtAJg9e3bM30uVKpWOr/Ow7Zce9ttvPwB69eoFwC677AJAt27dUvo96ZzfDjvsQI0aNQDo3LkzADVr1gTg4osvBuCzzz4DYNdddwVgw4YNnHjiid7/A2zbtq3IY8j09jvnnHMAmDx5MgC9e/cGYNSoUWn5vkzNr2zZsgB07NgRgLPPPhuAU089NeZ19913H6NHjwZgzZo1xf5eO7+klh12iOh8Rx11FADt27cH4KKLLgKgcuXKed7z+eefA3DyyScDhduu/7XtF40pqoZhGIZhGEYgyaiiWrp0aQBuv/12APr06RPz/MKFCwFYsmQJEFlRSzVYunQpAM2aNSvy9//XViQ2v6Kx9957A/DWW28BeErWaaedBuRVWlOFbb/0sGjRIsApH9u3bwege/fujBs3LmXfk8751a9fnw8++KDI71+xYgUArVu3BmDjxo2F/oxMb7+pU6cCcOaZZwLw3XffAdCwYUMAfvrpp5R+X7rnV6ZMGQAGDhwIQP/+/fU9+v4879G1sEePHoC7DhaFbJ9fJkyYAMCll14KwNq1a73o1bp164r9+ZmaX6VKlQAYP3484PbPwvDFF18AcMABByT9nmxvPz+lS5f2zq3lypUD4JBDDiny55miahiGYRiGYYSOfFuophopqNdeey2QdwV5zDHHANCyZUvvb3rNvvvum4khZoSqVasCkVwlzVXKsZgyZQoAn3zySWYHlwRXXXUV4HLhRKdOnQCneMybN8/Lu9q6dWsGR1g8pNz8+uuvgFNUBw8eDKRPUTVSS/Xq1QHYa6+9Yv6u3LKdd94542MqKtu2beO3334DoEKFCjHP/fPPPwC8+eabABx33HF53n/ooYcCkWMSoHnz5gBs3rw5PQNOAS+++CLgFCspj9p+YaNr166AU1KF5inVu27dukAkonjEEUcAcM899wCx18awoXxpXdP3339/atWqBaRGUc0U2ibJKKkrV64E4NtvvwXgpJNOAqB8+fKAOzd9//33KR9nuunTpw9NmjQB8u7TqSacR7xhGIZhGIZR4smYolqmTBmOP/74uM99+eWXgKtaVb5DmTJleOONNwB44IEHMjDK1CKFWCsvrYarVKkCRBRVVQb+9ddfMe896KCDAFfRm20aN27MzJkzATf+RMqGcgCPP/54Txk+//zzAfjjjz/SPdS04Ve9w4oUG1Weyi5F++f+++8PwMyZMz3VUfmAyhtcsGAB4KrJg0irVq0APNVGSN0Pk4rxySefeOcEVcOLr7/+GnBKqiqJr7jiijyfU7t2bQB22mknINiKqp8999wTcNvzhx9+yOZwCsX+++/PXXfdBcCPP/4IwJVXXgk4RVVo2+y2225eDmfTpk0BOPzwwwF477330jrexo0bA7Bs2bK0fs+zzz4LwIcffgiQ8B4hSLRo0QJwDho77hh7G6VrXP/+/XnmmWcAmDFjRsxrdL3/+eef0zrWdKDrQ8+ePb3rwogRI9L6naaoGoZhGIZhGIEkY4pqz54986yWOnToAMDixYsBlxson8Mdd9wxKwpc7969ufHGG2PGKEeCwtCzZ0/AKVgvv/wyEJt3+tFHHwF4yrHUyGwrVVK1TznlFADuv//+uL5w0fz999+Ay4OrU6cObdu2BeCxxx4DoEuXLgD88ssvKR9zqnnwwQeBiJ9hWFGlcY8ePTyfv/r16wNuGwupa59++ikQUd/kgKB8ZHk/StHR/lGUKvJ0Iz9DP1IBJk2alMnhFBuNO1F06aWXXgLgsssuA+IrqjoOlX9tZIZzzjnHu77dfPPNQF4lVUjxv+qqq5g2bRrgcoyVC6hjOR20atWKp59+Gsh7nh4yZAgAzz33XKE/VzUqDz/8MBDJ0/z4448Bl58bBgYNGgQ4FyP5NOu3uvfee4HItV3q68EHHxzzGVLNlasaJmW1X79+QMT9QF7UypNPF6aoGoZhGIZhGIEkY4qq8hqjUU6qVprCn6+Zabp06eKph48//jiA57c4bNiwpD9HfrFSUP35YFWrVuWss84CnJKqishsV/urGjHeynn58uUA3H333QB88803gFtVvfPOO0DEqUG5R8rTvfPOOwF499130zX0lKHtpWpjPYYBddGSKhyd66x5SdmQyvb7778DTuUHOPDAAwHn96fjQblyypd86KGHUj+JYpLI00/Kf0lDOao6LqPRts12pKYwrF27NttDSBkXXXQRt956KwBPPPFEUu9Zt26dp87Js3OPPfZIzwCjWLBggVelLgcXoWua/FwLs43UYUyK8kEHHcQtt9wCwGuvvVasMWcDRV0TUbt2bS+C6HfqUDQnTEqqriGXX345ANOmTfOuHenGFFXDMAzDMAwjkGRMUW3VqlUeRUo5K8q90vOzZs0CIp2BtmzZkqkheowePdpTOoXUp8Lgr5j0V40/8cQTntKsns76LVLZMacwqMpbFanRyCdVuXAFKd/r16/3Vlzdu3cHXN5gGBRVIZVbKuXuu+8OBDPPVjmpc+fOBZxf5tatW73cIqk0//77b4Gft3r1agBuuOEGwP0G4vXXX0/BqDOL/HBLCieccALgKqi1f0YzduxYAE/BCgOF6doTdE499VTWr19f6PdJ/dc5SBGOdKPIlyIoyqlUfnRJUruLiyI3Usr1W9WoUYPddtst5rXK7df5NEw0atQIcPdpzz//fMa+O2M3qocffngeg39/sYN+gN69e3vPy94hk4wbNy4lN4oDBgwAnNWUzH6VQA7O2H/MmDFA9kNzalGrpgxi+vTp3liTucERssoRKuQJI0oHURODiRMnZnM4cdH28xu6d+7cudDFQ3vuuadnSSYLKxVNtWvXDnCFV0Hi6quvBpzhf0lFF0idI9XaMRrdvBYmZclIPUW5SQV46qmnAHjyySdTOZwCUZhe4W0d/9dddx3gCoNXrVpVrO+58MILgXCF/itWrAi484wa4Pgbi8RDKVNqGqAmHUFGDW/UsELbfM6cORkbg4X+DcMwDMMwjECS0RaqhWX06NGeKXeYVlyyo1LyvJRkKcYK9+fk5HjKrczXpbam29A5EYlCg0uXLi2UkipkZ2Jkhnr16sX8WwWLhVFTFd4fPHiwF+5RSorCzCqSCyJhbbFZWP73v/8B5LGNU4HGtGnT6NWrFxAuY38jOEiJV/RI7b9lvXjNNdcARS+A9ts2hQEVRunYipdqUxC33XYbAOeddx4QzOYjug7onkC2oTfddBOQ2dS3/8YZ3TAMwzAMwwgdGVNUb7zxRvr27Qvg5b3J4kKPauGoVVylSpW8u3k1Bci2dVUyyFpK7SZlzSSksI4bN85rPSa1VYqqimGUF5otuyoVtt1xxx1Jv0crsalTp3qGxiIMOTlhRu2IhVb/FStWTNrkXav96II6mcgHWUkt6bRu3RrAO49K7fajfEZ/nnnYUKtGI3uoYEaNBtR4QOcDneu7deuW0PRdzULUllWtcMGZ5etzw2D8r3Oq33JKqGHDhAkTvCJsPSpHVYXV2a5JyQ9du7WtFdVWE4pMYoqqYRiGYRiGEUgypqiOHDmSCRMmAIlNbtetWwfEVqgeffTRgFuVhUFRFa+88grglKl4qydVDIqRI0cCLr9106ZNQPYU1RkzZgCFq/TfccfIbqXK8GhkbRUmpHaHIfdRq15V50uxeOihhzx1RO2A1QKwVatWgGvLeNpppwGRJhRS9MOUI14SKV++vJcbpnOiH7W9DLv9lizydP7Q8SerNEXXgoyOJbUYBnj11VcB5wCwcuVKwDVjiIdacOo3WLRoUeoHmwS6HskNQMgEvlKlSl4jGEVBdV5RVE62TdGogUGtWrWAzCmqyrfUtfnzzz8H3Db6888/E75XDW50vMnVR5Zdd911FwDvv/++9x6530hR1T4uB4EgKqvKS5ZCrEiO370pEwT/ymsYhmEYhmH8J8mYovrvv/8W2C5MlW9a5US3ZRw0aBDgvMvCQFG8WFUFqVWLVmvZonbt2km/Vqt/mYuXFLQtlOMZ5DxbGYS3bdsWcKv+008/3Wt3+uOPPwIuv0xV41JtNN+NGzd6Valhxj+vMKG2qPfccw8NGjSI+xopjMotzk8NCgOqLFf7Tm233377LWtjShYdS2pjq4hGbm6ul+snpKzKRF/Ri6+++gqIKFl16tTx3h/9mkzzwgsvAO76e/311wMu1/K0007zIjFS9BWBUnvw/Mhke+py5cp5x5J8Yffee2/A3YPIkD+ef62iVVKTpRRv27YNiJ1vkyZNAKewC/02ij4GCeWGn3jiiUDeWqJsYIqqYRiGYRiGEUiCdzsPLFmyBIhVQJTLmWlFtVy5cjFjSZcfoVQErVz1PWeffTaQN5c1XaiVq6owu3XrBsCIESPyeL3JL1b5NqoMjaf8vPHGG0AwOxkli1bK2WjrW1i0+lXLxaZNm3r7sh+1qvRHAJRrFXZ07OoxGYUn21SrVg1wOYHxvBrVolkd78KupBZEttTEwuCv6n7xxReB2GuZ8lb33XdfwDmqqDZD7aW///577zVyg9HnZQupv8rllJdv69at80T/dJwp71Qd/Y499lggVq3MZLSjUaNGXutnv5Kr7lKjRo0CnEr63HPPJTxvKIfTzwknnOCdU/3KafQ2DhrKhZfqq39nE1NUDcMwDMMwjEASSEVVFXjLli3zvNcyzVlnnQW4XsQXXXRRyj5bSuTGjRu979GjVs7yYO3du3fKvjcZpOhqNaWORnPmzMnjoaluWqpcjIdcC+RmEIY8s0RonurOpKrIMKAVfDyaNm0K5K2ulsdvSeGDDz4AIupIUFE1so6XeEqq8o6Vt1/SlVSheQcZncfvv/9+IH4EsFKlSoCLdvjdNnQ8RqNIV1C2tc4R6lC1++67e/uuH10DdO7Xezdu3Jinq1omqFy5shexTBRl0nH31FNPARFVOFkP6TJlygCRXF25GQjltybqAJlNdH1ThEZOBPPmzcvamEQgb1T/+OMPIL6N1SWXXALA448/ntYxqGBG4SaNSca9uqGLZyshg38Z4+rfCu/rZDZo0CCvNaVuUBUqyJYdldDOKSuj5s2beyF9hUBk8KxQuOyOom2cdDM3ffr0DIzaKAw1atQAoGvXroALvy1YsCBbQ0oJRx55ZLaHUGSUPtOhQ4c8z+l8OHz4cMDZ+ZU0VIASRnTT6W/yEo22o4rg9Lhq1SoAHnnkkTzvufPOO1M6zlTzyy+/JN1SU+3Bv/jiC+9GVe1IM5HasHz5ci9crxtVFaHKUsp/DunVq5dnr6ntpWJUFRxrcbnbbrsBkWumFv8qxFWqRDYLkxKhfVeWYd27d8/mcGKw0L9hGIZhGIYRSNKuqKoIaPDgwTz22GMA9OvXr8ifJ9PydCN1yd8G9YorrgCgS5cuQKz1jd8GR+qI/h3PJkfKrIqNgmL8qzCN7HFOOOEEjjnmGMAZVfu3hcIk0ZZGSroPI5qv3/Bffw9T6D8eMvGWhY6MrItzfAYBFSCGCR1n11xzTcLXyEpHKQyJ0PZs3bq1p4r4UwjUfjXZtrqZRLZUYaQoNkuKsKmAR58xduxYL/Xqhx9+SNEIg8Nnn33mqedVqlTJ2PeuX7/eC8HruFDR1M033wzA+PHjAdhnn30A6Ny5s7edlLogRfWII45I+F06vnRca3sGjVKlSnmqtn6LIKXamKJqGIZhGIZhBJK0K6rKAdlrr708w2PlbioP019go/wQGc5C3pai6UbKphRh2Y1oBag8zegVtH81rX9LLdUK5aOPPor5Dv//B5FXX301oToqCysZyotly5Yxc+bMtI8tXcgY329rFEbj+Gik3vnz6LR9k801Cyra5/LLEwwap59+OhBplRqPTz/91CsCk4WOFB5x+eWXA86wO16Ro6Jayi8PIh07doz5t47DMLTP1rlB+fxvvfVWntfomqjz5aOPPgq4NtXKRx04cGChWleHjccee4xOnTpl5btVEPvll18C7ribM2cO4Aq6o1ERXLI58KtWrfKacARVSRXHHXcchxxyCAADBgwA8m/tm2lMUTUMwzAMwzACScaq/rdv3+7loSxatAiIVP0BjBkzBnC5HkcddRQQWZ1K3dFrMo2q8mXSLEPnZNQaOQRILVXLvJKGqgX9psazZs3yqinDSCLnhfxykoKMFDa1d9x1110B12BDynjYkbF4mMjPPgwilcVff/014I4zbb9kUI6cDNuD2PRA81EDEaHzqPIKg4zO8TfeeCPgFLratWtzxhlnAM7+x9+eWm4OYc8RT5YVK1Z49Q7Kq5bCunz5csBdf1O97aUWysxeuamKgioqURjk5qD7m06dOqWtQVCqiW7xmu2mEvEwRdUwDMMwDMMIJGlXVFUZ3r17d68dnJAZ7j333APEr4q/7777gPieqplEqujLL78c8/hfplmzZkB4FcaCmDFjBuD2vWyYU6cSmXOr4luq2tChQ4FwtIZNBuVyqllHGHj22WcBV3ErxxC1Mi5VqlRc8/94KC/yk08+YciQIYBzdAiikiqUIyd/X7FmzZpsDKdI6PeWA4oUw5133tmrEpeHplRX+aYG0YEhnfz000+eYqqc3ui2quDyJVPtI6u6GNW+qPZETRfyQ9cDeYN/9dVXgFP+FfkIEwcddJDnLKH5BAlTVA3DMAzDMIxAknZFVd1TmjdvzoMPPggkn0M2YcKEwHfk+C+jDhx6FKooDpIPW1FQfrQ6kWi/VU5nmNhtt9248sorY/4m9aKkdQ1T5xu/ohpkBwpFkSZPngw4f2VV9tevX997bcuWLQHnJqJcf6l4Uk23b98eKocKRa2UP6jzytq1a7M2psKiY+nSSy8FXG7w6tWrPaVq2LBhQLCdFzKF8iHlTa38aymec+fOzcg4lD/8X0PRi2bNmnmRNXXhDBKmqBqGYRiGYRiBJCe/FXdOTk5Kl+MVKlQAoEWLFoCrNKtXrx4Ar7/+OgCzZ88GIrlW6smbCnJzc2OMTlM9v2yT6fm1atUKcJWtpUuXBlyuY9myZVP6fbb9is7ixYu9XGKtmHX8Zap7mG2/cGPzCzdBnp8cR3RNkTPJsmXLkv6MIM8vFaRjfnXr1gUi3e4UMZw3b15xP7ZI+OcXTUZvVLON7cjpQa3XRowYAbi2sx06dEjp99j2Kzw6ES1btowyZcoArohDF4NMYdsv3Nj8wo3NL9z81+YXjYX+DcMwDMMwjEBiimoJwuYXbtIxP1nCvffee56JdpcuXYDMN6Cw7RdubH7hxuYXbv5r84vGFFXDMAzDMAwjkJiiWoKw+YUbm1+4sfmFG5tfuLH5hRtTVA3DMAzDMIzQka+iahiGYRiGYRjZwhRVwzAMwzAMI5DYjaphGIZhGIYRSHbM78mSnqxr8wsXNr9wY/MLNza/cGPzCzf/tflFY4qqYRiGYRiGEUjsRtUwDMMwDMMIJHajahiGYRiGYQSSfHNUM0Xp0qUBGD58OADXXHMNALm5uZx00kkAzJ07NzuDMwCoXbs2Tz31FAB77LEHAK1btwZg7dq1WRuXYRiGYRglF1NUDcMwDMMwjECSVUV1p512AmDYsGEA9OjRA4Dt27cDsG7dOn7++efsDC6N7LrrrgBMnTqVE044AYCcnEjB29KlSwF4+umnAbjnnnuyMELHzjvvDMD9999PkyZNYp6bNWsWAMcffzwA33zzTWYHZxiGYRhGiSYrN6oK9esGtVevXjHPb9iwAYAjjjgi1Deqmme9evUAuO666wA4+eSTAahUqRL+zmCNGzcGoGHDhgB88cUXAEybNi39A47DjTfeCETC/Brrt99+C8C+++4LwIknngjAY489lvkBFoB+Ty2KFi9enM3hGFlgwIABAAwZMgSAc889F4ApU6ZkbUzFoVSpUgCcffbZAEyaNAnAOz616H3mmWd44YUXAJg8eXKmh2n8x6hQoQIAAwcOBOCMM84AYMmSJQBcffXVAKG+phvZwUL/hmEYhmEYRiDJ8St6MU+m2FB2hx0i98UjRowAXNGU+OmnnwBo3749AIsWLUrl12fMMFdK46233grAhRdeqO/TOIBIisMzzzwDwNtvvw3AXXfdBUDZsmUBePLJJwG49NJLC/zeVM7vlFNOAeChhx4CYJ999vGKpqRSSjH+448/APjnn3+K+nVJUZT5bdq0Sa8F4PfffwecKvziiy96zz/77LMAfPLJJ6kacqH4rxk6Z2p+//77r74fwCsKvOSSS1L6PemcX4UKFbj88ssBOPXUUwE47rjjCnzfli1bALj33nsBuOGGG4o8hkxtP51XNM+jjjoKgJYtW8a87qijjuLNN98EYPz48QA8/vjjRf5eO/6KToUKFTxlX+lsfqSsnn766YC73qcK237pRefLQw89FIAuXbqw2267AfD1118D7r5h1apVhf58M/w3DMMwDMMwQkdGc1SvuOIKIK+SqpzUu+++G0i9kpopqlWrBsCcOXMAOOigg+K+7pdffgGgc+fOnqInBg0aBDhFNVsce+yxAFSvXt372x133AE4BTUMSGmR+qR84SpVqgBudZiTk+Op1vfddx/gLNHef//9jI03E5QvXx5webuiS5cuQCR3GiI51cqH9EdennjiCQD69OkDwMaNG9M34BQThqI/RZ+aNm0KRKIzKloUigooEvDXX3/F/H3w4MHsueeeAJx11lmAi9gEcXtprg888ADg8vQT8cEHH3gRn4kTJyb1HWXLluXvv/8GXNFuptC5VBGbdu3aAfD6668DULVqVSD1SmMmOP/88z0lVfP57LPPALjssssAt311DtbrgogKnq+//nrvb7pP0TW7c+fOANSsWROAWrVqAU75f/jhh6lTpw4AHTt2BODHH39M99BTzqhRowB3DtF+vGzZMvbee28A71HF39q3FdEpLqaoGoZhGIZhGIEkY4pq/fr1vZxNP6r6V75mWJHiWLdu3bjPS4W66qqrAKeAALRp0waAvfbaK+Y9yq3MNPpePa5Zs4bnn38+K2MpDj179gTwcmm0KvRbbXXv3p199tkHcG4UUrcvuugigDzqd5ioVKkSHTp0AODaa68FIk0cCiKR6qTfRHlnUqHDgBS1IKHj7JBDDgGgd+/eQPzcdOWt33bbbUDk2IzHli1bvIjCAQccALjzS9AU1T322MNzJtBxqHxy5aEqz1a58K+99lqBn9uqVSvA7fPly5f31J7NmzenaPTJIbcXRcvkyCAFVYqq3CgUgQwDY8eO9dTCvn37AvDee+8BTmlUgxhd64KsqCrqJMeQaAqyjNQ5U4oruEhGMrUmQeHwww8H8K4bis5069YNiJyH9Jxyw6W2ytbSFFXDMAzDMAyjRJN2RbV+/foAzJ8/n8qVK8c8p9V+SfH4W7ZsGQAXXHABAKtXrwbgo48+AvJfTamS3p8LmC3PuTJlygBuPD/88AO//fZbVsaSCqTOaOXnrw6++uqrOfPMMwGXk1OjRg0g0pgBXM5xIgUrSOhY04r3f//7n5cblixLly7Nsz/60b4dRHTuCQPyK5ZKKuRYMGHCBK+qWjn8W7duzfcz999//1QPM21cdNFFnpIqFUZq6AcffJD05yi3XqqeHAPGjh0LRBqXZFpJBShXrpx3vdMxpfzahQsXAnDeeecBTol7+eWXQxXFkkIqJVXIA1yKqtT9IKNtpH1R18Oiovcr51/HdRBRxEFRMkVhFAlWRGfr1q3cf//9cT9D0QDNc+bMmYC7JyospqgahmEYhmEYgSRtiqpWEKoKjlZTP//8c8DlOpQUJkyYAETUY3BVj9G5qImQoiqU55KtjlTnn39+Vr43m+i3VpV///79ATz/yldffRVw+a1Brs698sorAdeNKT/eeecdAH799VfAraRnz57tHcfKsVLulvLKg6yyy8vPn+fdvXt3ILnfJt1ISb3llluAvF6vyusvioovH1KAt956Cyi6opFJlKNZkJK6ww47eMfmOeecA8DRRx8NuN9L+ZBqTZ0tRo0a5W1b7Xc333xzzGvkpa1c1gEDBoRGUc2vlkIOFtmqtygK33//PYCnGKpqH/C2if9Y0jkxXu6/VEqpk0F0HtG5Xj63inDoWvLwww/neY+iVvqd2rZtC7j6Ds2zuK3gTVE1DMMwDMMwAknaFFWtcC+++GLvbz/88APgegCXNOQvumLFiqTfozwy/V5adcsBIciVkSWVdevWATB06FAATjzxRMB1HOvatSsQcQfItBdjQWjVm58irnxdqVGvvPIK4Lp4RaNcP6mQQqprtpWq/FDOsY4pPWYrShEPVfkr11fKqirCi4JUReVYA8yaNQtIf/e4VJCoM5x6ySs6N27cOM8fWfO68847AefckW2HB6lr5513HvPmzQPyKqlCEQ09ZttLuzAMHz48YYW3zpEF5bsHkeuuuy7mMR7yh5X3ajTKh1Y+chCVVCE1VNcF5Rr7lVQdf3Xq1PHy5bVt5TWrXPuXX345JWNL+Y2qThzxpF4doEEuwMg0MrcWCo8sX748G8OhRYsWgLNz+i+jG1YVASjUo5uJGTNmFKrQI53IVFuFfB/SWEsAACAASURBVP7k/+3bt3PuuecCLsSo8FYiWrRo4dn6hBGFsPzIED8IyD5NF7Ti3FjtvvvugLsRr1SpkhfyHzduXHGGmVEUrr/99tsBaNasGYBXTKaGFOXKlfOOPxUgqaA1KKiAqmzZsl5ouCTSv3//hKF93fiUFGS9pEI9NZuQfZMW/DvuuKNXVKSmHEHmwAMPBFw7VL81l25Q+/XrBxCzP+uaoveoQDBVWOjfMAzDMAzDCCQpV1QVIvRbUU2dOtUrNkqE1DytmMGFgcJQBFBYmjVrxjHHHBPzNxWAJNsSMNX8+eefgAvXhCkBPl2sXbs27t/POOOMrCuq2j5K1peSqu0nQ/62bdt6IcVElC5dGnChrFdeeSWPMrtt2zYgHGG8I444Iu7fg6SopsJ+Tq1wpXDo3Lt161YGDx4MBM/g38+UKVO8AgwpqFKG1ZrTHwq/9dZbvfScVBmLpwrNRXZZw4cPTzqSGMZzrs4L0SitxV9clKpwcCYpU6aM1w5VKmmPHj3ivlbpevfee69XqBomdK5YvHgx4FIatE9Hz1uRc1lWpet6aIqqYRiGYRiGEUhSpqiq0ESFJ35uv/12z8pAyf7+FYk+I3rlrORcPQoVtCiZN0xons8//7ynhkgRuP7664HsWR/J1kWWWsU1Oq5YsSJAgWpeGNAcNKcgKB8yz1bhkFBOlFqdJoNsuRK1AAaXN+g39Q4TYW6FG42swtS8QvuAoiIXXnhhUm1Gg8C6deu8HFTlm5522mkxr1GRqhqnZDuakR8tW7YEXIRMx00yhCFakQxqCesvMpI9ZRioWbMmEGkIo7bGiVAxtXJUVaQaFjTuhg0bAq4gVwpyo0aNAHfcPfnkk8W2nUoWU1QNwzAMwzCMQFJsRVUVcLrrjmfRAJHV/tVXXw04RSoZqlSpEvMopk+fDjjD7HhmtJmmWrVqgLOaOv744wFnYC1UNafXA9x0001A9vN3pMZoHDI6rlSpkqfg6DV+tBJTvuThhx/u5R2/+eabgMuj+/LLL9Mx/CJToUIFr81i9erVY56TOvnuu+8CLmpw+umnew4A2eLII4+M+/dPP/0UwGubunHjRi93UUbwyiETe++9d8Lv+fHHH4FwVK+WK1cOcOcmGY5LBU60/4YNuahISVVURir6Sy+9lJVxFYWGDRsmzCmeO3cu4OaZbcupZPj4448Bd75LJqKk66Iev/vuuzSNLnWonmTs2LExpvjxkJIaZEs7oUjnmDFjAHdNyw8df7oORh9/CxYsAGDEiBGpHGZK8dv4yYJQkUNdU1SHpBzWTGCKqmEYhmEYhhFIcvLLh8nJySkwWUYrqqLkVMr81u/xJ++5jRs3eisb5Yco7+Wqq64CnFqiNl8TJkxImOOTm5sbk1SYzPwKQr6xQ4YMoXnz5oV+/+TJkwEKXI0mQyrnJw9KtXIsV64co0ePBty2UDWnGjio3WN+ea3Kg9HvVphcx3RsP7FgwQJP/U2EVpbav+666y6vzWpBaD+uWLGip076Kcr81OZO/pF+tw2xdu1aL581EToe/YoywBtvvAFAhw4dgKId7+ncftEox1Em29puOtbOO++8dHxtxuZ31llnAa4piBwe2rdvD0Ra36aDdMxPUZqXXnrJq5D3I6/Ze++9t7hfly+Z2n6J0PlUytXYsWO9uaeCVM5vjz32AFy+d9OmTfnqq68A11BE0Zyo7wNcpDHVzWxSOT9Fl+QpWhjk5LHTTjt5+7c8q3WtLIqqnO7983//+x+Ad50X8mLWPdeHH36Yyq/18M8vGlNUDcMwDMMwjECSthaq8fjll18A58c1f/58wK0g46HWeGrJJdRFQV1MpJ5MmTLF+550oBxc5eqpa9GOO7qfUsqU8pK0svQrcuDU46ChHGBVqw4dOtTrMPLbb78BLj9Y/pvx5udHK0y1iFRloXJYM406OrVs2dJTOuW8II9bKXDKfRSFyZWTEvn444/H7CvFRSt+jVX+u6rQFNFqqnI0VRH+9NNPA85fVIpctPuGKlqz5UaRDIqu+KvFSwrKfX/iiScAd9xdeOGFQPqU1HQin9djjjnG8+J89dVXAXdu1fVC+3Y6z+/ZZL/99gOC4SaSCB1jct1RXvH69esZOHAg4CIaibj77ruBSFQtXtvmILB161bAnROrVavmjXXq1KlA4mu3rvsNGjTw7kv22msvwCnQDRo0AFLjo5wK2rVr520/P6pp0DUgG5iiahiGYRiGYQSSYks7WmU899xzQOJcy4kTJ3orKXnLhYlddtkFcKsp5dmI9957z8sZk9qq1abUw3ioMjmoaL69evXy8pKkgviREiAV/N133/V+t8aNG8e8RrmV0V3IskF0L3vlTEm5keJ4yimnAHkV1SOOOMJTKhN1r9L8C/LgKy7q4KY8RX3v4YcfDsDKlSs9lUCPfj/DSy65BMjbAQjCoWLJGSRRhW62HTWKQ4sWLbzohnLA5S2tPtthQnOIVr/VuVC5cI899hjgXAwU2XjggQcyNcyMcvDBBwMuIiXngCChY0yOE2Ljxo1eRbteI2VRdQ7yTde1oHbt2oH1wlV3JuVNV61a1fMWl59vQWzYsMH7HF07pKyqZiHbyHlo+vTpCSOhil5n0zu72DeqfqN63cCp6Els2LCBzZs3F/l7dEMnixJdgIUk+nit3IqDLvi62VQ7P0n2uvkeP368t1PqpmT48OEFfv79998PBNdKRq1rL7vsMmbMmJHUe3Rzu3z5cu/mx38QaIGjBgPZZocddqBChQqAMxRXQZkOZrVAVIiubdu2nhWJ2j0++OCDMZ8riw+F4v0pLKnGb/ulG9j80M22QqzRKNwzcuTIFIwuO+gmWwVhYULnu0mTJlGqVCnApUxdd911QMQsv6gUZDmXLg477DDAXQQh0gAlGi0UlR6kYk3d0GrBVVJo0qQJ4Bbz/nNJENDNmhbmOnc0atTIO8erbbMKTbUd/Q1+gtCCuiC0mC9MkwLdjE6ePNlr7iO0WM52y18V3ermMzc317PQWr58OeCs1YKAhf4NwzAMwzCMQJKyqg4VEGm1q0Ib0atXL84991wAPvvsM8Cpifkhy6dWrVoBcOihh8b9XoXFUp3wq7CFirakpOrfsgUCV9Cl5gd+9B4lUo8YMcKzAvIb4wcNWY4kg7azHuNxzTXXAE6lzBZSQuvVq+cZ+fvbAEsZv+GGGwDXnOGGG27wbEwUrpQZsl9BVnvSu+66K+VzKC6PPPIIALVq1crznBLsNf4go9/eX4yi7RUU9T4ZVDil9qilSpXyzjXaDwujRu2+++6AC6MrpKnjWkWFmULnQPHrr7/mORdo7hdffDHgrgEqTg3DPlkYFDJPl/1PKtD1VZFTRZ0aNGjgFQolSrFRIZbs1OIVjSlyquhBUIut4qFo65AhQ4D4zVhko5ntVCptP0WMP/jgAwYMGAA41bxLly7ZGVwcTFE1DMMwDMMwAknK7amUR6S8FCmrxx13nJfrp8dEBs/JMGvWLMDl1aV6dS3FT9YMUsj8SqoKjAYNGuQpqXrt+vXrAVewM2/evJi/DxkyxCtckZoVVEX1/PPP599//wWcfYxU4HjFN4mQpYdW39lm6NChQCS3U/uukDLgzzVW29SxY8d6VizKS9b+ILQvKL83SAqB8stkNO5ny5YtXt5SGNh///2BvGq27JsmTZoEBNtiS8WF2helMD3//POe6pko11/qiPLC69at66k8KuaoWbMm4JQxGZBnm19++SVhy1AprVJUVXRUUhRVKcQq2jz66KOzOZykUCSzMIVtUlL9rToBTjrpJMCpkbJgU45/kNlzzz0BZ9kVraQqf1zR46C0xZWdoXLUe/fu7bVEVf54kDBF1TAMwzAMwwgkKVdUZYIu9XDhwoVAZEUv1UlG1X4uuOACwOVvzJo1y6vsHDVqVMxrVaUqK6RUst9++3mKhszZlVsoJfXkk08GXNV/dLs4KaZSY/25V6qkX7p0qbey0WpaFfNBUt4gYifzzjvvAG6sTZs2BZwqIzVRFkm//vqrNx89qh1bUFA+zvjx4wvdfGHjxo3efqFH5RYKVXcGydVBKl3Lli0BV6XqZ/ny5aGyklO0w4/2UynhymUNIn379gViq+EhUuHuV+u13aQUS9mJbiihKIjaWyqXXw09itLKMRXIIUXUqFEjYZ6+HF1KKrq2SelPxqkjjOjeQNZkJ598sncsKoda+bn+tupBQrnEajmqvFO/1eSXX37p3QMEbZvq/kl5whUrVqRixYqAi+roOamu2cQUVcMwDMMwDCOQ5OTX7jInJyfxkyEkNzc3psww0fyuvPLKPLkxUlDPPvtswHltRhv3Sm2Vqub3tPRTpkwZzzhZqxeZ6RfFIzDZ+YUVm19qkPq/cuXKfF+38847pzRike75ff/994BTPISq/dWMJF3ejamYn1o0y21EikxhkCH5999/z+jRowGYM2dOoT/HTyq3n1S1uXPnAhF3F9U1qNpYyrB+C/mM6lys96aKTJ9fOnXqBLj56Zqi/MxUk+3zZ79+/QBiagH8TQHGjBkDJG6gkh/pnF/FihW97SVf30S5xLruDxs2jIcffjhVQ0jp/FSboQhOTk6OF+mtU6dOzGuV566GOOnCP79oTFE1DMMwDMMwAokpqiUIm1+4ydT8VD2eKCf36aefBiL+lfmdHwpLuuen6n45gbz77rsA9OzZE3AeyOkilfNTXpg623Tr1s2L5sg1RUyePBlwXc+k6KTaUzod20/57MqzBZfLqCiTX32Vyqz821SR6fOLHDWUKy4XjuJ0GsuPbJ8/tR3lgtOmTRvPx1rKf3FI5fzkkiF3msaNG3udCxOhOgWplUE+/uIpqv5zvZRv5Qun27HAFFXDMAzDMAwjdJiiWoKw+YWboOSoqgq5T58+Kf1e237hJh3zk2rao0cPL19f1cfaPy+//HLA5RanWkkV6d5+mteiRYsA5wer3M0777wzlV+XB9s/k+eee+4BnJ96PORGIXVSymMY9k95pWqexx57rDefs846C4i4vkD65uMnP0XVblRLEDa/cJOp+ak9oULkelT74/79+wOwYcOGlH6vbb9wY/MrHkrZ+PTTTwGYMmUK4Fpup8NqMRrbfslz2mmnAXiLp3333dcr1pSFlp7LlPXUf237RWOhf8MwDMMwDCOQmKJagrD5hRubX7ix+YUbm1+4sfmFG1NUDcMwDMMwjNBhN6qGYRiGYRhGILEbVcMwDMMwDCOQ5JujahiGYRiGYRjZwhRVwzAMwzAMI5DsmN+TJb2qzOYXLmx+4cbmF25sfuHG5hdu/mvzi8YUVcMwDMMwDCOQ2I2qYRiGYRiGEUjsRtUwDMMwDMMIJHajahiGYRiGYQQSu1E1DMMwDMMwAkmgb1Rfe+01cnNzY/5r1aoVrVq1yvbQDMMwDMMwjDSTrz1VtnjttdcAYm5Ib7nlFgAWLFiQhREZRaFDhw5MmjQJgK5duwLw8MMPZ3NIReLwww8HYPjw4QCcf/75AHz33XdZG1Mq6NOnD+Dm9dJLLwFw9dVXA/Dll19mZ2BFpH379gCcffbZAJxzzjkA7LTTTgCsWrUKgJEjRwIwc+ZMfvzxx0wPM+2ULl0agMaNGzN9+nQAqlSpAsAOO0S0ie3btwNu2w8cOBCAf/75J6NjTRVvv/02ADk5EYebY489FoAtW7ZkbUyF5dZbbwWgV69eAOy6667ZHE5cNCbtY6Jx48a0bt0agHLlygHQpEkTAI488kgAPvvsM8DNc8qUKWzevDn9gzbyULlyZQBefvllAMqUKcOyZcsAdww1atQo5j1TpkwB4K233gLg9ddfz9jxFWhF1TAMwzAMw/jvkm8L1UwbyuanpN58883F/vx0GubWrl2b+fPnA7B8+XIARo0aBcD7778PwN577w3AueeeC8DgwYO99991110A3HjjjUUeQ1AMgevWrQtEtucee+wBwIcffgg4peOXX34p9Odmen7aPlI4dtttNwCaN28OwDvvvJPS78vU/LR9HnzwQQBatmwZ8/zGjRuBiAIye/ZswKmQxVFA0jm/Y489lrlz5wKw447JBYo2bdrkHYuaZ3EIyvGnc8htt92W5zmpJf7zfsOGDQGnOscjKPOLpm/fvgAMHToUcFGOpk2bxvw7GbI9v3QrqqmY3+TJkwEXtSgOq1at4oQTTgBgw4YNxf68bG+/dFOc+e27774A9OjRA4D99tsPcFGoaL766isAdt55ZwCqVq0a87zOIatXr/YipnfffTcAf/zxR7JDyoMZ/huGYRiGYRihIxA5qlJL/UVSt9xyS0qU1HQilXTOnDleTt/ixYsBOOmkkwCnbBx66KGAU6U+/PBDFi5cCLi8wJUrVwLw1FNPZWL4aeGAAw4A8NRUgL/++guAf//9NytjKgpSUCtUqADkVaHCSvny5YG8SqpQ/lLlypW9/LKPP/4YcHlKQeOcc87xlNSpU6cCcM0118R9bbt27QDo168fzz//PODm/Pfff6d7qGlDquj//ve/pN+jvLQ///wzLWNKB1J0unXrxvXXXw+43Nv169cDLioQJtq2bZvtIRSLb7/9FoBvvvkGcNtAeZDK7W/QoAEA9evX9yIZRxxxREbHWhC777474I6Lf/75h7322iup9yr3Pzc311MuN23aBLjIzcyZM2P+nm4uvvjimLHpGNJ9xrp16+jfvz8AgwYNAmDevHmAU1Rr1aoFwAsvvABAnTp1GDBgAODykM844wzAXe9ThSmqhmEYhmEYRiAJhKKqvEU/QVdTwalSlSpV8laFv//+OwBXXHEFAL179wacY4EUjzVr1nifU7FiRQA6duwIhFNRLVOmDACnn356nuekyOm3CSNSwsNUSRyPFStWxP37ddddl+f5zp07A3ir7aAqqnXr1vXUDx13ifKgx40bB0Tm8tNPPwEuF14KwbZt29I63nQgxUpRnvyYNm0aAJdddhlQvNyyTFOpUiUA7rjjDi/qIaSIb926NePjKir169cH4MADD8zySApGx44q+6NRbu3q1avjvnfMmDGAcxdp27Zt4OasXM5FixYBLuLw66+/cuGFFwIFR9YS5YEDXHTRRUCkYh7wcnTTje4vxHvvvQe483vjxo29c7xQfrcedV1QpLhx48ZejqvmofPKmWeeCaROWTVF1TAMwzAMwwgkWVVU41X5Axx33HFZGE3RaNasGRBRYKTgzJo1C3BV1aoSX7p0acLPadOmDRDuPMg777wTgC5dunh/kwqplVaYUV6RXBzCij8fTOq9XCqi98E333wTcHmtQUYRimQV759//tnbpsp1HDFiBEAo/FXlU6nzSn7nF6Fzk9ShMObkKmITvZ1VNa4cvDChHMCyZcsC8Oijj2ZzOPkiZw09FgXlhbdt29absyKJzz33XDFHWDyU562oxKmnnpqR70m3J7d+82uvvRZw3uBPPvkkAK1bt/aU4ILGEr0PSBmWt6ruYy6//HIA7rvvvpSM3xRVwzAMwzAMI5BkVVFN1Ao17N2nnnjiCcApcPlVumsFohySV155Jc2jSz3y5dSqOJrHHnsMCJeiWqNGDcDlFquiWCvOsOPPRZISGU/Nl2oV9Lzc+fPnc/vttwNuFa/8y0Tk5ubyyCOPAOlTTtLBWWedBcDEiRMB54mo/GG5HrRv395TTBXtGDJkSCaHmlKU9yblP9pntHv37kA4ox3yqxRhzuMvLDq3Ku8428TzFS0symudPn06N910U9zX6J4gUxGNJUuWAM4b/NJLLwVc9z5wSqmq/ZNB3ca0zypnXM4BOhcVVzE2RdUwDMMwDMMIJFlRVJWb6idMualCSlNOTo7XU/zZZ59N+v3KVVHv7WHDhqV4hOnnyiuvBGJ9UyGyyho9enQ2hpQSpDBq24Q5fxhcx6b9998/5u9S/sPMc88956lqJ598MgD16tUD4KOPPkr4PlX5CuUHygXg3XffTflYi0u3bt0Ap3z8+uuvgMvPVNeg3NxcL1cs27l/qUD+m9EKpHKoE1WahwF1RxPq9mNkHnUHa9GiBQA1a9YEIvnfyscUimCsW7cu5u/yRj3vvPMSfo+81ovSobEoSMGVp7uuy1KQDz74YCZMmFDoz1UNyqeffgq4+gflXeuxuIpqRm9U8zP2h3CG/HVSueGGG7w2frL5KYiDDjrIM/pXi1GdeMPAQQcdBCQ+ICdOnBhjwRV27r333mwPoVioqE+WMJ9//jkAX3zxRdbGlCo+//xz77h75plnABfu1o2ArFJ08WnXrp1XPCVzcoXtdOINMkpXUCqAFrmymnrmmWe84qkwo5C/Ct5Kly7tPac5J7JcCwMyS//hhx+AknE8hhVdr9S0pigoBU5WXtHoZjeehWMm0TlCqXlFRefUsWPHAvD4448X6/MSYaF/wzAMwzAMI5BkVFFNZOwfRiVVfP311wAsX77ca9koY2O18/Ozyy67ABHFQ/+vsGWYuOqqqwAn7wuFIsOqQKplqh+1yA0jOTk5eZRv2af9/PPP2RhSylFrvxkzZgCuVaoUVilVsk8rW7asZ6vSs2dPwCmqYeLpp5+O+bcM1XV8hpU999wTcNtPDUWUgjN//vxi2SRlG5nmKzIlFV/KakklulWsUufmz5+freGkHKUeRaeKKcSvYqaSTqoLj01RNQzDMAzDMAJJxhTVVq1aJTT2T6So3nzzzYFvoyqj6RtvvJGXX34ZcIqO5ivFSkUASlo+9NBDvfkpuToMtG7dGnAtG/1I4QlrgYOsNUoSFSpUoGvXrjF/KylKqlDbTLVyVFs/fz7YN998A0T243feeSeDI0wvykdVC9mwI5NyKali1apVAJx44okZH1MqibYGAkpUPn80sixSkZ8UR3CFx2HICS8I5chffPHFQKyiquiVGhwor1zFnGox/vbbb4eqnXEiNPdatWoBxc8hN0XVMAzDMAzDCCQZU1TjWVL5lVSpi9Emufr/oJutL1682MtXPeSQQwBnYizlSo0AZAkxYsQIzy4iTCiftnLlyjF/1zxT1TYt06j61q90yJQ6zEQbWW/btg1wuYwljTPOOAPAs4vzo7y4sKmpyoH3K4myiDnttNMyPqZ0ICcUtaf2E8ZzZn7o2qZ86ZKGlFS//dHnn3/uOf4EnQ4dOuRpzCD3kGiLp0T069cv5lHb3G952LBhQy9ikEkqV67sXRd+++23Qr9fUWQ/qXKwCP8V2DAMwzAMwyiRZMXwX0qqcjilmiZqqQpOkQ1qU4A///zTczX45JNPAKegSmnt0KED4FaWffv2zfQwi4W85Y455pi4zyvfSPMPG6r41upQPpUy/A8zhx56qPf/UohbtmwJOGXx22+/zfzAUkROTo6nxA0fPhxwrhtS+NUSV3lhnTt3LpLJdTaoXbu2Nz+/CjN58uRsDCnlHHbYYQCMHDkSgFKlSsU8L89H5a6GHSlx2p4lITcRnKuNtld0TirA2rVrgUj78KA6qagt8fTp04FIm3Dtj+lo/CJ/1XRfO9XsRZ7a2gdbtWrl7X/jx48H3Hklv4YpYvfdd0/5WKMxRdUwDMMwDMMIJDn5rQ5ycnJStnSI/h6/oupHqmmrVq1i8lX/f0zFGUPMm1M5v2jeeOMNAI4++uiYv6tz1d133w1EWoymknTPb9SoUYDLIfMjRTld3bUytf3U0UgtKbXPSUletGhROr42rfMrV66cl/vkbxsq38bjjz8eSN+qPp3za9eunad+KIKh+aiaWjnIOj4B6tSpA+Rtg1gU0jE/KTvz58/Ps9381K5dG0jNXOKRzu1XqlQpr1rar3LLPUQdqsK4f0ZTvnx5wLXn3XXXXQEX9UhXW810z0+Ko45Dv5KqNr79+/cHUr+fpnJ+bdq0AWD27Nne3xSJKijCFu91f//9NwCvvvoqAAsXLox5j6IE+f0mRZmf8rzlCFIU5fO9994D3PZUFz9wnuNSXffee2/ARbMaNWoEJLdP++cXjSmqhmEYhmEYRiBJe45qPB9Uv5IqhVV5GgUprkFlxx0jP2eNGjXiPh9mf8799tuPSy65JO5zysVdvnx5JoeUNhIp3T169ADSp6imk02bNnluFPKlfOqppwAXwVCXH+XmhqF/usY6fvx4zx9VyrdfnZBXsXLFp06d6nWkUv5qULjhhhsAt89Vq1bNi0pJvahXr17Me+RWoRzdMNGoUaOE+cJS6MKa++6nfv36gOtIpfqLdCmpmaBZs2bMmzcPcF6hOh7VjUnXiTDk/K9cuRJwymCNGjW8cReUo6qIzkcffcSTTz4JRBwOIHPODrp3krOLtonG7ncdmDZtGhMnTgSc4t24ceOYxzlz5gDOuWDevHle97+99tor5vvVFS9V+3RWiqmEbkgTFUhFh/2DbmOx0047eTulblS1w+6zzz6AC43rhjxMNGnSxDNuFmp2oAvjpk2bMj6udBBdeFSSkI2RHtXK8LLLLgPg4YcfBmDmzJlAJNTz4YcfZnqYSaET7x133AFAxYoVOeWUU4CCQ4rRYbeCwumZRjfRukFVKG3z5s2e/ZRCx9OmTYt5b5MmTTI1zJShwhtZGEUjE3hdOEsKakQhwtx4Q21gBw4cmOcGVTZqYTTz/+677wBnhVazZk0vdcrfslioMEzbN10pOMkgaz5tE6Fz/wMPPAC4lIOVK1fy119/Aa6oVjexWjQr9WbKlClAJI3hpJNOivl8LSZTnf5noX/DMAzDMAwjkGRVUfUrqZKr4zUHCGor1erVqwORAoAjjjgCgMsvvxyItEMD1x5NicVhUlSlBj/yyCN5nlORSlBVt+Ki8IiS44PedKKoPProo4Br8Tt27FggEurRMRq09o5SF5XO8NRTT8U9b0SjlAe1WIXgpKuULl0acM00qlWrFvN83759vbEOGDAAyLs/SiFXSkAytjLZRu1e+/Xr5yk4P/74IwDdunUDXIOKkoK/EcXUqVOzNJLioyhM27Ztve1UGCVVlmTal9XuH9klGgAAIABJREFUuG7duoArOFMhb6aJTkeR3VYiNPZsKqmJuOuuuwCX7pVfUwF/IxTdx0hRVfGfGqtEIzU51VZrpqgahmEYhmEYgSTtiqryUP02U+BUUuVuxiue8rdZDRpa9Tdv3tyzb1CxjVbOS5YsAfASj7O1OiwKsmhSLlI0YZpHUZDCoyT6IUOGZHM4aUcWKqJatWpeIYSUL7UfzTYqRBH+sef3noEDBwIRpS4o+7BUUDVh8BdsHHXUUVx77bUA1KpVK+5r1K4wDEqq2ktLQY6eS9euXYG8Fj4lhY4dOwJOeVOjlDChfGgVM4Kbh19JrVixIuAiig0aNADglFNO8dTXRNGqbDchUcRm0KBBXv64/7hTnqcip0FCv+szzzwD5K+kJkI1KLoHyK+tvVRWRelSdb0wRdUwDMMwDMMIJBlTVBcsWJBHMY2nskZzyy23BDY3tWnTpgBcf/31QKQC129bJLsqVbb++eefGRxhalCFcTTff/89AB988EGmh5NWZBvjz53Wqv6nn37K+JgKi/Y5WYqoLWxOTk6e3Dgdj8o9Um51NFJOlN8ZFEVV1atCeY3gcm2V56Y2gZdeeingFPKhQ4cG5pisWbNmvs9fcMEFCW1xVG186623pnxc6ULb5sADD/T+pmpxGYyXNGSFplbUsgMKI1WqVAFirw9qsOHPcdRrtM3z47fffgNcTURB9wjpQuc7qcT16tXLY+QvO7/zzjsvCyPMHzXJUK7o7bffDrjc/mRQnvyVV14J5G30s2nTJi8qoIjQ/fffD7houeyx1ORI4yospqgahmEYhmEYgSRjVf/HHXdcwqp+v+F/UFXUaFSlq1WWquOjkUG+lDq1UA07MveVMlDUVVLQ0Mpf+XNCla1hUFTlQiFPX3n5VqhQwcsRS5Z3333XM8JPdbvf4iLzcKlUffv2pXnz5oBrE6hcOCG/SuV6jhkzJiNjTQa/eX8yyERc/rFr165N6ZjSiaq9o5E6U5ChehipW7eulxutiNTo0aOzOaRiofxhtUXt2LGjp8D5HSsSsWrVKq+iXMqffhOdt7LFsGHDADj44IOByD7pN/xXW9IgomNp0qRJAF57YuWbah+MRoq4tp+u81WrVo15nbxS+/Xr5/0Gft9n/UaqHZAvbVExRdUwDMMwDMMIJDn5rV5zcnJK1NI2Nzc3pkwtFfNT1XD37t095U05jVJ7tBKTK8D7779f3K+NSzrmpzlF5y9OnjwZcK3xMqWopmN+0Wg1OGPGDMCpPsr7TDepnJ/UbqmH8fDnjMvPUdv3ww8/5N9//y3qEPKQju2nvLdHH32UZs2axTynatylS5cC6W/FWZz5ScV44403AJezqg4ymzdvZvbs2YCr6pdakSm1O5XbT9tNuWsnnXSS1/pWrW4zTTrPL3vssYencsnpoCBfzlSTjvkp771Pnz7e9a1FixYAbNy4ESBPa1z9+9tvv82Ta14cUjk/tQtt3bp19OcBMH/+fMDlpmqe6aYo89M5UdcBtVn2t1D1fW7Mc7oeqiOVcnOLq5L68c8vGlNUDcMwDMMwjEBiimoxUZeGSZMm0aZNm5jnVqxYAUCnTp2A9Ck5It2KY7ax+YUbm1+4sfmFG5tf8owcORKAa665JvrzAFd7kmnXhv/a9ovGblRLEDa/cGPzCzc2v3Bj8ws3qZyfbNNGjBgBRAqKZP/26quvAsk1GUkl/7XtF42F/g3DMAzDMIxAYopqCcLmF25sfuHG5hdubH7hxuYXbkxRNQzDMAzDMEKH3agahmEYhmEYgcRuVA3DMAzDMIxAkm+OqmEYhmEYhmFkC1NUDcMwDMMwjEBiN6qGYRiGYRhGIMm3iXlJtz+w+YULm1+4sfmFG5tfuLH5hZv/2vyiMUXVMAzDMAzDCCR2o2oYhmEYhmEEErtRNQzDMAzDMAKJ3agahmEYhmEYgcRuVA3DMAzDMIxAYjeqhmEY/0+LFi1o0aIFCxcuZOHChRx55JEceeSR2R6WYRjGf5Z87amMgtl5550BuO+++7j00ksBeOWVVwBYs2YNAKNHj475t2EEmWeeeQaA8847z/vb6aefDsBLL72UlTElw5AhQwDYc889AZg+fToAM2fOTPie1q1bA3DAAQcAcPvttwNQqVIlAA488EAAFi9enIYRG4ZR0mjevDkAI0aMoFmzZoA7p27atCnmtY8//jgAb775ZgZHWDiOPvpoAI499tiYv69evZopU6ZkZAymqBqGYRiGYRiBJKOKarly5QCnWrRv3x6AdevWxTyuXLkSgHHjxvHrr79mcoiFpmrVqkBEecnNjfjvnnjiiTGPUlqXLFkCwIMPPgjApEmTMjnUtNOkSRPAKXHt27dnv/32A+CBBx4AoHv37lkZm+jbty8Ad955JwC9evUCnOqdDNqejzzyCGXKlAHgn3/+SeEoM8v1118PuN+mfPnyAN7+DNC2bVsguIrqrrvuytVXXw1AxYoVAejSpQsAf//9d8L3afuVKlUKcHOWGrt06dL0DDiNdOrUCYgoIT169ABg8uTJAHzxxRcA1K1bF3Dn4nfffTfTw0wJNWrUAGD9+vUAHHXUUYAp4EbmadiwIeDOHRUrVmTbtm0A7LPPPgC89957AFx++eUAlC5dGgimoqqUp/nz5wNurGLbtm15zq1jxowBYNCgQSkdiymqhmEYhmEYRiDJiVZN8jyZghZdtWvXBqBbt26ce+65gFtdRH0PEKvgAGzYsMFTXVOx4khnC7IyZcqw2267AdCmTRsAzjjjDACOP/54wCk9mufTTz/trTykCBSHTLdYU85K7969ATj55JMB2GmnnTSePNtWCo8U1sKQivlJWTr77LOByDYAuOiii5L+jEsuuQSIKKr3338/gKfmFYdMbb9TTz0VcKveww47DHDqoti+fTuzZs0C3Py+/PLLIn9vuuenPLCOHTsW+r3Lly8HnKr89ttvA7B58+akPyNbLQ5r1qwJwHXXXQfAVVddpe8v8L2KWFWuXLnA1watheORRx7JyJEjAaek7rvvvgB89dVXhf68oM0v1WRqfjr/T5w4EYAOHToALqI4bNgwAF544YWUfm+2tt9BBx0EwD333AO46+CmTZu83P7XXnst5j1SX7/77jsAfvjhhwK/J1Pzk3Kq/NN27drp+wt8rxTkH3/8EXDXmMcee6zA91oLVcMwDMMwDCN0pD1HVTkZ5cuXZ+vWrQDMmDEDgGXLlsW89vDDDwfc6rhKlSp07doVCGYORzRbtmzhp59+AuCpp56KeZTSqvzMm266CYALLrjAywORohfkXDHl40pFlNot5UYr5rFjxwKwcOFCKlSoADjFSrlxJYWLL74YSI2imk5KlSrlrW71mEhxu/XWWwGYO3du4I+7ZFBOuNQLsXjxYm+//Ouvv4C8VblBQg4EimCcf/75AOywQ0RvKFu2bIGfofmNHz8ewFPMw4TOmZMmTfJyVK+99lqgaEpqtpEKfMsttwAuYpMf2heC5CQjBxzlNKri/bPPPgOgevXqgLt+rF+/3rs/CDM9e/YEnJKqeoXrrrsuj5Iq3n///cwMrhCoxkRRT0Xe/AwdOhSIKOaKmE+YMAFw9wjVqlUDnHoulX3cuHFFGpspqoZhGIZhGEYgSbui+vLLLwPQoEEDr8JUuSuJuPnmm4HUV45li99//x2AO+64A4CpU6cCkapkKQGvv/46AC1btgSCV218zjnneDk4WhkrZ2XEiBEA9O/fH4itgNfqWa/9+OOPMzNgI4Zy5coxePDguM9JTZQPqfJ2w6hOQd6c90ceeQQI3jFVWJTXpihTYVAOfOPGjQH45ZdfUjewDKO81Bo1ang55zo3BRk5S7Rq1QqAM888E3AuInLFic4FXLBgAeCcOKR6KX9Q+eXZZuedd+bhhx8GnJK6aNEiwClzGuu8efOASBSxJCiq8mLWdnvnnXcAeOihh7I2psJSrlw57/5M8/GjfXHUqFEAbNy4kU8//RRw9SrKk5erwR577AHAgAEDAFNUDcMwDMMwjBJG2hVVVfobjtWrVwOR/LA+ffoALr9Hq+qgUK9ePSCSD6YVoxTT4cOHA261FO+9V155JQAffvgh4PIFjeAgVT9MCkB+JFOdWpL49ttvAZgzZw4A33zzDRDZnsoZ07EaZiVV1xLVMIBTV4POAQccwA033ABA586dY57TuVEdDaMrpD/66CMAz49aitS0adPSOdxCM2HCBM8/+7fffgOc8v/HH38Ars5EziGqlg87+fk0Bx3VjDzyyCOeEu5HbirKnVZlfzRSVhVdlaIq/E5PhcUUVcMwDMMwDCOQZLQzVbLsvffeQCTXTGpBSUI5Sddcc42n/sydOxeADz74IFvDiotW9E888YTnaqCq/kSemlJhlZ8MrgOUkR1OOumkPH9bsWIFEDx1JlV8/vnnMY9hp0GDBjH/VhcYdVn7/vvv87xHTgdSWeXoEMb8f/85ZOTIkYHvQCXVcM6cOV51vxRGucLIBWbDhg0JP0f7sCrL//333/QMuIjIOxycmvbJJ5/k+x7l24adwjjZ6PgT9957L+B8RzOFIrfKKY1WU7dv3w64yK9yV+MpqZkiEDeqMsKXzcNll10GREIhYTyhyopB8zrmmGMAV2wkS4fSpUt79hVqDqDClqChm+v8UHhKBVTVq1f3TsKvvvpquoZmJMEJJ5yQ528KOSZjNh1Gfv7555jHMNOvXz+v1a1QUWq8G1Q/sgL0W3SFAYUTo0P+4Io6gkj0DSpELKi0sFdr7bVr1+b7GYceeqiXGrZx40YAryDyzz//TP2gi8App5wCRBpGPP7444Bb+OpGVNf1XXfdFUjORi1MaLHUvHlzAGrVqgXA3Xff7b1Gc1Zb5+effx7I/Llpl112AdwiN951XeeIQw45JGPjKggL/RuGYRiGYRiBJKuKqmyOnn32WQCOPvpoAK8xwMSJE2OsjoJMw4YNvZWyVplSUv12OVpZ33bbbTz66KOZHmrKUIhfxv8XXnghAHXq1AEidkf+UEdJw2+2XpiWm5lA7TUvuOCCPM8pFNmpUyfAtflLRqELA1L4dTwqdUWJ/wrBBhmlbPTs2dNrbSh0flEhpuyA4qEQY5jwm/mLMJj7z5w5E3DH2Ny5c7niiiuAvOOW+qpGDrJxql+/fh718bnnngOcBVJQyM3N9doOS0WWDVd0S+1oSkKkA2DVqlUAtGjRAnD3NX369PHmrHsA2cQp0pipcLrOHWpdHq9l+BNPPAG4CEZR0Lko1ZiiahiGYRiGYQSSrCiqTZs2BdzKS7mcWn1o9V9QY4AgMXLkSE/hKAjl8oRNTVUCttqFypZKZtT+1ePgwYM9q5V169ZlcKSZQ4qHrGeUY6xcIDV5yFbu8Y47Rg7xeLZnyqnSowo2vv76ayDSHlYKQBipUqUK4NQtoSKBuXPnMn36dO//g8Q555wDuHOgX00FZzklZfiqq64CnJ1M2PEXT0mJVH5fEFFUSXmKOv8NHDjQ2046X6qQRQqx/i6VLbpgSkXFQSsuVnvUzZs3e2qdH13vNPYbb7wRCEdEIx4yt3/yyScB1y7Uf/3Lzc315qjCVbXczvT1UFGzeEoqRM4zUlKlEBeFRE1liospqoZhGIZhGEYgyYqiKiuEChUqxPxduWO33HJLxsdUXMaMGeO185Nxs1S1gQMHAq6VnNS3RYsWhaoaXlW2MqzWClKPUjpkMn7MMcd471G7wKChFm8tWrTwcqyU16mVsjj//PMBOPLII/N8jraxn+XLlwPZs4DasmULEKkarly5cr6v3X///WMeZ86cmccSKchICVaOu3Lj/Bx44IHeY9u2bQGX+ydnjmyjYyhaSVVeovKgK1WqBLicxn79+gHu/KNK8bCRKDdVCmSQc1P9OXqqqs7PNUXbScrjrFmzgIj6piYHmnPQ5r5mzRogko/ZsWNHwB13Uuh07hs9enTMexXNCDKqQejevbu3DXW8qSVuogYjS5Ys8RR2/U6ZRs4LilJL7RVLliwBnJl/UVFb3HjuMuDarxYVU1QNwzAMwzCMQJKTX7vBnJyctPQiLFOmDOD8xxo2bAi4lYnaxKn9ZqrIzc2NWU6ka36JUHW8clvWrFnjVQr+/vvvxf78dM6vU6dOnkG1VmXy8pPvrT937Omnn/ba6knFKs7KMhXzO+uss4D4eW5SrKTo+BXVZNB2fP311wGnQiezokzn9mvRokXC3EWpyv48yC1btnhRgVSQqeNPKoK/Ynr33XcH8JSf9u3be3NWLln9+vWBoilXqZyf9lPlw7344ot5FFUp+8r1l0+lVOLohhupIFPbT0qcv7pfFfTpIhXz0zlD5zld68DlnEoxnTp1KgBvvfVWzHvEihUrPC9L5XVGe3MWlmxf/1588UUA2rVrB0SOv1RGmlI5P10DVJ3vbwkKrjmPDPIbNWoU8/zw4cPp27dvUYeQh6LMT04nir740baYPXt2ocdzwAEHeKq4nH6kMgvl/p9++umAi3bFwz+/aExRNQzDMAzDMAJJVnJUlTenKnnlVilPQquXZcuWeepqSWDKlCmAy1c65ZRTvFXmcccdl61hJUWVKlU8xVsdjaT6JFJJFy5c6CmqQUGrYHmF7rXXXt5z0W3kkkUVutqmynMNmsvBm2++6akEfpQTHq8LnFwDstk+r7AsXbo03+d1zLVp08br0CLF/8EHHwRcPnm2eOGFF2Ie46GI1MqVKwHnQx12OnToEPNv5aaGAVW2H3zwwUBsTqDfRzsRUo4rV67sqedhqmXwo5xO+arqehHk1s1ya9H1Kzc319sWXbt2Bdx5RC1Ule/pzwPNBooMxXN7AdeZav78+Ul/pubZrVs3IOIgoCiVH0UN9Pvlp6QmgymqhmEYhmEYRiDJamcq5YWp0lY5PPq3Oh+VNG6//XYgoqi2bNkScB1oUp1XlipWrFjB2LFjgUhHLXD5L4lQ5XKQkFeoVpzKj27Xrp3nQ5nID1Bov2zTpg1///03/8femQdKObZ//JNoj6gQUSS8tihLtlKSkmg5JWQtWoRQIqKEQsqaZMlbljYJRQpZUkQp+5YXyRKpiGSp3x/n933uOc+ZmXNm5nlmnqnr88+pWe97nvX+Xtf1vSC/vSvnzZsX9/Hy5ct7vnhh+ePlknnz5vH7778XeSyRU0CUefzxxwGnqMqdIqrnkkRI8fcr//no5VuSahoP5UvLp7pWrVqeV/XixYuDG1yWkSIun9jJkyfncjhJ0XWhbdu2RR5funSp1+Huxx9/LPKcv9I9Wd1PttD4Y3OkwSn+9957L+Ci27FoP1T+qvLklduvmoZ4PPvss4CLjvvPr+mS0xtVPzNmzACiYxETFgpNzp49m5YtWwLOEiiqvPrqq16BUGlR+CuKrF69GnBtQ+fOnesVLJTUtlcHYcuWLb0wjw7ufGn5G4t+AxV8KUQXJXSRU7L+2rVrM/7M1q1bFyuAyFVjhkz45JNPivxfIbp8I5HBv1IcNnf8tlybNm2KnB1VKmy77bYAnsWdFvWygosiWiTofKMxFxQUFLtBFR06dACKh/yjOM+FCxcC8dP1VLinexEVHPtbwMeiNrj63bQ4DuL8HIuF/g3DMAzDMIxIEilF1U/dunVzPYSEyO5BK4pUkJ2Twv0Ar7/+ejADiwAtWrQAChOpVQTw/fff53JIpSIdNVTFRvvssw+QWfu5MJAycOKJJ3pFcH4FTo0A/A0BNm7cyEcffZSFUSZGdj8ylFa6yQknnACkl6Qv+ypZz4Db9rfeemv6g40IihbkG/6Qfz4VUQXBzTffXOT/Q4cO9dI68hGlih100EGAa2gQtXNkLH5LO513lDIWi4redD6R4qhzphoY5QI15fHz4IMPFvm/rg9Nmzb1CtdjC4zjsWTJEu+vGgnosbAwRdUwDMMwDMOIJJFSVFUEoJyIqFn8gCu+mThxIlC6fDC1itUKs6CgAChcgS1btgyI9iqztKjISK3yNm3a5FmPBZVUHTWkxEV1++20004ATJkyxWsxqmIGJb5LEfC3S/3777+9/TxXSDnVcaa/UoVVgAnOXFrHlB/Zyki5q1Chgvd+7bOJCsvCRmb9t9xyC+CKumQdlixXUTZxYtSoUWEMMVR22223YrZUsnrb3Onfvz/gilVU4DJx4kTPTD6fUAGP/3wie8Yoo5apIp7VlI5NNcDxm9wrT1MNcXJBvXr1AIrtP5dddhkAF1xwAeCaoMgqNB5q8XvRRRcBrvWt8nezgSmqhmEYhmEYRiTJmqLaqFEjL0cu0Z24P9dDd/JRolq1agDstddeQKFSBckVtUsuuQRwyqqMgz/66CPatWsX2ljT4YorrgBcvmJpHBj8v4Wq/a+++uoSzdeNcFEO5+rVq6lduzbgqor114/M/UeMGJGFESZHCqcURamh8fLXpSKUlm+++caz3Ro/fnwGo8wcWaPJTFsceeSRALRq1aqYqqrzpSI0+Uxsxb+/dermiux/1GhD18U+ffoAxXPJ8wW105TR/3vvvQe4iEeU8VfDq01x9+7dPUcQHaNHH310kddqe0XhvPnwww8DzqFGyrDfSisZageuqFbYeajJMEXVMAzDMAzDiCRZU1RnzZrltfpbtGgRAPfffz/gWuYddthhgMtN1aogSqxZswaAzz//HHAeav48sVi0mtGKRLly+h2ihMZWp04dwK0SYxUnKTkdO3YEnAKgisnbb78dgJEjR2ZhxEYypEoVFBR4+aYlNWJQe714LVWzjfJNpSzWqFEDcC1rK1So4L1WK/9Eyqqq4VVdPWHCBFauXBn8oNNA7hiKIslVRDm5L7zwAgMGDACgefPmgGtPKANu5dvmU7tboe0Lzr9xc0cqstpc6rr30EMP5WpIgSAzeUVGlf8dz1w+atxwww0AnHTSSUBhJBhI2sp9xYoVgGuKovbcuURV/8qnVf1PIjZu3OjVkWg7qcFBLpVUYYqqYRiGYRiGEUnKJGv3VaZMmcB6gbVs2dLzUfO34JLiqLZzyteaNWtWUF8PwKZNm4qU8GUyPyke8hFTxWYsaqs5bNgwwM0vrAr4IOYnFU3VxjGf5a2Q/Z0qtKKUQhBWdWeQ2y8TlPfz8MMPe5Wdyj/OhGzNT5WfqlbV/9UZTlXjQefIRWX7hUWQ81OUQm1RO3fuHLcCGZxf49ChQ4Hw2vmGsf2Uc/zNN994uam5ci0Ie//U9pP/pjw61QlIinlYOY5hz09546pFUbRD0UZVi4dFkPOTf7M8z6+66qpiHqs6XypnNWwlNZ35SRFWtCkRq1at4oEHHshkeBnjn18spqgahmEYhmEYkSRriiq46nApOD179gRg/vz5gFu9xOtDGwSm6JSMVv3Kg1MObpMmTbzcRuXWvvbaa4DLJVbXoLCIyvbLd0U1V9j80qdPnz5eDpwcOcaMGQO4iI18csMijPl17twZKFQRVUWdq2r/sPfPhg0bAvD2228XeVz5yS1btgzy64oR9vx0fZfCr7/y1w4bO7/kN8kU1azeqOaaLW1D2/zyC5tffmPzy2/CnF+zZs14/vnnAdhmm20AJ9AoZSrs4tqwt5+KwnQjfttttwEwbty4IL8mIbZ/5jcW+jcMwzAMwzDyjki1UDUMwzCMzY21a9d67ZaVMtW7d28gvFS3bKMi4WyF+o0tB1NUDcMwDMMwjEhiOaqbETa//Mbml9/Y/PIbm19+Y/PLbyxH1TAMwzAMw8g7kiqqhmEYhmEYhpErTFE1DMMwDMMwIknSqv/NPQfC5pdf2PzyG5tffmPzy29sfvnNlja/WExRNQzDMAzDMCJJpHxUr776asD5y9WrV4+//vorl0MyDMMwDMMwcoQpqoZhGIZhGEYkidSN6sqVK1m5ciW1a9emdu3adOnSJddDKpGqVatStWpVxo4dyzPPPMMzzzxDnTp1qFOnTq6HZhiGYRiGETh33XUXmzZtYtOmTYwZM4YxY8aE9l2RulE1DMMwDMMwDBGpHNUpU6YA8OCDDwKw44475nI4pWLQoEEAdOvWDXnS3nzzzQB8/fXXORuXYRiGYeQj5513HgAPP/wwAD/++CMAderUYcOGDTkb15ZM9erVATjrrLMA6N69u3fPE7Yff6RuVP/44w8A1q1bB0CvXr247777APj9999zNq549OzZE4A+ffoAhWN/4IEHAPjwww9zNi7DMIwtjQsuuACAKlWqAO4aonOyEW2OOOIIAEaMGAFAw4YNAbjxxhsBuOOOOwAicZO69957A3D88cfHff6HH34A4KmnnsramLJBy5YtARg5cmSx5z7++ONQv9tC/4ZhGIZhGEYkSdpCNVeGskoBKCgoYKeddgIKC60yJQjD3F122QWAefPmAbD77rsD8Oyzz9K+ffuMx5gJ2TYE7tq1KwATJkzQ9wPwxhtvANC2bVvWrFkT2Pflk+Hx1lsXBis+/fRTwO0v55xzTsL3hD2/1q1bA/D0008DsM022wCwatUqwCkAUgQefPDBQNNXgphf48aNAejUqRMAu+22W7Hnvv322yJ//SxYsACAqVOnsnz58lSHkJAg5rfffvsB8M033wDw999/A07NADjyyCMB2GOPPQC8ws133nkHgEsuuSTVry0VUTz+7r77bgAuvPBCAMqWLQvAv//+C7h9uTTFrWHOr3z58lx66aUpvads2bJeGpn44IMPAJde9sQTT5T686K4/Vq1agXAvffeC7jrabdu3QAYP358qT8rjPntsMMOABxzzDEMHToUgGrVqgFQu3btIq8tU6bw6xX9fe+99zj66KMzHYJHrraf0i/atWsHuPlv2rSJhQsXAk5dVlQ8Hczw3zAMwzAMw8g7IpWjmg8ccMABQPEV+nPPPZeL4eQUJVf7E6qPOuooAE4//XQvx3hLQSrltGnTAKd6tWnTJmdjAqhVqxaTJ08GnNqr7SXVQCqG6Nmzp6fS3XTTTYBThnPF5ZdfDjhFVUgljYeUVqmveu/IkSMy3dGnAAAgAElEQVS96E3nzp0DH2s6NG3aFIAbbrgBgAoVKgAu9zIZBx54IAB33nknAMuWLQtjiDmjatWqANxyyy0ANGjQwNu2/siglNVdd901iyNMzLBhw+jbt2/K7/PPa//99wegR48eQGqKatTo06cPw4YNA+Dnn38G4NxzzwXgsccey8mYdE1TTqxU0yZNmpT6MypVqgRAo0aNOPvsswF49NFHAdi4cWNgYw0bKaiKFG+33XYA/PPPP0DheaZ///5ZGYspqoZhGIZhGEYkMUW1lJQvXx7AW0Fopfv9998DW1Z1qVaXqshUTo5+I6kZ+n++oXFLeZs0aRLg8gWT0aJFC8ApqMqRCyLHOhOqV6/u5QtXrly51O858cQTAaf0XXPNNQCMGjUqhFGWzBVXXAG4ytM333wz5c+QslpQUMBll10GuJxQ5ZQFmbuaCopASJGX+p0MRXdeeuklwLWi7t69exhDzDrKrZaa3qxZs1wOJy2ksmXKyy+/DMDbb78dyOflAp1TRowYwZw5cwC4+OKLAfjqq69yNSzAKalnnHFGyu994YUXAJdPvs022zBu3DgA5s6dC+TuvJIKOr5Ue6LrhfK+FbHJlpoKpqgahmEYhmEYEcUU1VKi5gP+1fzEiRNzMZycIAeG22+/HXAqwUknnQTAgAEDAKe+9ejRw1uh5hPyxr3tttsAp6RKWY2HcpG0glYu0nHHHQfAL7/8EspYUyFRvt6tt94KwL777gu47aecJHC5ksqdfOSRRwBYvXp1KGNNhBSJTJQJvXfUqFGsWLECcNtWFfW5Vj5kcF4aNIfp06cD0KFDByD/FVXth8rDLE2ebj4ht40vvvgCgLFjxwLw+eefJ3yPcsb//PPPkEcXPFLGdazNnTs3Mkrq4YcfDri8zGRIOZUjihwYdC5UJfw+++zjvUfRx2SuL1FBSqk/8iaPW0VssokpqoZhGIZhGEYkMUXVKBU1atRg5syZABxyyCGAy7vRClM5kPPnzwcKK94LCgqAQs/KqCNfWCnDyp9SZXg8VDF/zz33AE5JVRX5Z599Fs5gU+TXX39N+Ny1114LuGpOVb5ecsklXotgoVW28pDzmd12281TCaSgJnMPiDrvvfce4HIA8xUpqa+88gqQuFL61VdfpXnz5oDLp0sntzBM5AKy7bbbFntu9uzZAJx55plZHVO2URdHRagUhRwyZEhCr+Nso31M7gNCvrWjR4/2HtN1QedLP6pHiFVUt99+++AGGwLlypXzIjLythXK31cULReYomoYhmEYhmFEksgrquqCkOuqaaHuE0IV75s7gwYN8vovq4+28jL96DcqV66c59251VbRXhM1bdrU8wpV1b+U1WTed1IjlT8nZTVqfZ5///13L5dRuarKp1U1p1Du3C233MJhhx0GFF9lSy3wKxD5gCr9O3Xq5DkAnHbaaUDuc1O3ZJTDqJxUHXdyWNF+Ka/N119/3XvvM888AxR6N0cBRRz69esHQJcuXYq9Ro4OUlvV1SeRUpdv6Jxx1113AfDll18CMHjwYMDlVkcB5f7K9zoTdE149dVXM/6sbFGhQoVi53ih67y6LOaCSN6ofvTRR96/1VIwKiFUvwGz7KnSpW7dugDsvPPOgDuh1apVC3CFLosWLcroezKloKCA9evXA3DBBRcA8N1338V9bby2vLJ6ShZGzwVKHL/xxhv566+/ABc+XLJkSdL3duvWjYsuughwbUll3xQ1KleuXKyYSjeq/u2lwoKrrroq4ckrlyetVJEpvL9ZwPLly70bVC2ojNxw8skne60a/UVT/hvU559/PqtjSwct8LT4jYf2Q/2Vyb1SOBQqzzd0ntG5XjeoOpdE6QbVKGTvvfcu9pgWGFGwQou2zGUYhmEYhmFssURSUY0NgXzyySc5Hk1y9tprr5Tfc/zxxwNw5ZVXei1ZZf3k55RTTgHwCpl69OjhKQzZ4OSTTwagZs2a3so4kU2TWqfG8ttvvwEwa9askEaYGgrrDxkyBHAqzZ9//umFpJ599tm471X6ggqoBg4c6KmwsuHSfPMBGVMrfN+xY0cArrvuOsAVggCemt67d28gGnZbiVA4P5HllP6fTrMAIxjUDlWFUw8//LB3XAkV2lx44YWAK9qMh1LE/KjAMx9QUZX+NmzYMDKpDKVB2+/JJ58EXOGllNRcW1Bli6i07S0Nuh5eddVV3mM//fQT4Gy3dO7PJaaoGoZhGIZhGJEkUoqq1EkVO7z66quRV1RTQbmdakFWrlw5r/BIeYIqJFDrTeXVyYj4pZde8tosZgNZUZUtW9ZTqqQ8ShVR20m9NhYVOeRaaaxZsybgVJmDDz4YcIpL/fr1SyzY03xjX6eWnjLgljF+1Ay5f//9dy+fWvnPSvrX32QoyiG1OVmBWa5IpKSq3aq21ZaI8nTVuEQWVsOHDweyX0Sm88Kxxx6b8DVqDVsa/PuwjmtFDbJNELnqnTt35phjjgFckdi8efMy/tyw0HVbRbdnnXUWsOUoqWLgwIHFHtN1PWpceeWVgGsSAm6sia6HKhTUdQRcvUpY1wVTVA3DMAzDMIxIEilFVRW4yte8//77czmcImiF/v777wNw0EEHAa5FZtOmTRPaUcjw+N577y3y+Lp16zxlYejQoUBxdwNV3mlVes8993iqVjbMkseMGQMUKr7KYfSbwGs1pRWYch71G0UBKUdSUoXahMa2LZS1lD+/LV57PX2ufqcoVEjGo06dOp6zRGlZsmSJ50Kh/OQoW+dIQfXnpEppVWW/Hr/jjjs2CzsqqfjavhUrVgQK7cW0/XbffXfA5Zv997//BVJr1ZoJynWXqq3zZqwCozx8tWguDVLxateuXeRx2QYuXrw4vQFniBwM2rRpAxQeN2qQohaUflu4XXbZBXD5gscee6yX76hzks5fUaucb9++vTcvtRaVi8GWhmoZYq0so6qE+6+HUPwcr/nIoUjRgvPOO897je7VFG0N+rxiiqphGIZhGIYRScrE87z0nixTJvGTKbL//vvTtm1bwPnGySNVlCtXDnB38C1atOCll14Kaghs2rSpiFt/OvNr37494Dwo5fk3f/587zlV5UtFkMem37i6Q4cOvPHGG0m/T2rQ//73P+8xrWz8imoQ8wsCraquu+46b1WtfKVMyGR+yslTBaryoRs0aAAUmm4r90bVqiXx22+/eZ6H55xzDuCUx3QIY/spx23KlCkJnSX8KMfzvPPOCzTXNuz9U8eK2vZKdTriiCOKPK98TXD51UEoq0HMT44gfv9o5f3FonnKMD6eA4kqeLVfduvWDYAPP/ww1aGlND/91lJ05SQhtVdqk9ozTpo0yXPiKG2V8cEHH8zLL78MFG9R2qNHDwAeeuihUn0WBLt/KlIjP9V//vnHawlbEjpO33vvPS+3XjRp0gRIT6EL4/jTdpw6daqXb6y84JJ8qIMm19c/RYR1b1C2bFluuOEGwPmh//3332l/fpDzk1KvNr7/+c9/vOcUoZGjzS233AI4z/FkyPFB93ip4J9fLKaoGoZhGIZhGJEktBzV/fffH3C5lyeddJK3mvj4448Bl++pSlR/lefgwYN57bXXgMxWIkGiXCH5m0opPProo71OHFpN1K9fP+5naLVfkpqa72zatKmYN2KuULV/Mi/G7bffHnD5c1LetJJU7o4qJUePHu2tOqOK9tPSqqkAzZo1Awp/h6j435YGqaKjRo0q8ri/65S264IFC7x8yM6dO2dhhCUjX9E+ffoAzuewUqVKpf4MOWy0atXKU2Sz3epWSuqwYcPiPj9//nzA5ZiqhWVpUBed/v37e8qlolUzZswAYPr06WmMOjjWrl0LwIsvvpjye5Xfd8opp3jnKynGilBJef/iiy8yHmsmSCFv3769p6BmW0kNAjm6+PNL40Wc/c/JkeH8888H3DG7du3apJ3JcokiHrFKqh+5pVxyySVZGVMyTFE1DMMwDMMwIkngiqoqF7WyVT7l4sWLPaXRn8enakF1QlD+zTHHHMOIESMAuPTSS4Meakaox3v16tWBwupO5Q998MEHgMu5FfKxfPfdd0v9PaNHjy7y/6FDh3qfkw9of8gHVq9eDbi8nRtvvLHI89oX1YUqyqibjxwnwO2XZ5xxBuDydZWDJKVAEY5p06Z5KoFUySj6p6aKOlKNHDkyrVyqMFEup/7q/LnPPvskfM8ee+wB4Pkrz5kzB3CqZbY5+eSTvfELVbjLHSOd87miAjoOTzrpJO+5559/HnA5uNns3hcPKXPq7vbPP/8Uq/IviYULF3qe23JakRIWlUiVolDg8h51Hdc5Q7UU2Vb1S4Nyv3W/kkrkyY9faS1fvrx3byPlW1HXKKNujRdffDHg9mWhHPLvvvuOevXqZWVMgd+oKpFYdiEKPb7xxhvF2i6quEpmzdqxlS6wZMkSz2omasgAXfObPn26F47RhV74N7Sk9MWLF7No0SLANQMQ3bt3B1zIR0U7Y8eOTfmEl21iTbZ1Uckn1LhAYRGF4vyLhiijC5mK/QCee+45wN2w6q8WE7rIKwxWoUIFHn/8ccDZo21ObUffeustr6GG0gGiNj/97slM03v16lXk/wsXLgxxRCVzxRVXeCFhkckNqix0ZMsXazQutCDL9Q2qOPzwwwG3WOjVq1fKdovlypUrZgUYNWLP9RMnTgRcipQWTGpe8+uvvwKu8DTXaQvgUjOUQhIkFSpU8MQOFaXedtttgCuw1v1SttMldO5XgZ/S3cDZT6m4WCiNSNtvv/32S6lYMRMs9G8YhmEYhmFEksAVVSk5KsLQKjgWhc1l2aC7eykDUl5La1MSBdq1a+epMipGkSqq8J2UgFhVIVnSNjglVcbRUQ7777vvvgAceOCB3mNRUThKQ7Vq1QC3YhQqbMlGg4WgkOH5hg0bgMIwlEI5KjaSUixVcdmyZYCzF1NaC+A1e4ia4pgJCxYs8P6tFIB8nJ/UO6k2CmPmiuOOO65Yikis+XksKoxq2LAh69atA1wRqgrLFHmLh1o/Rs0A38/QoUNLragqCpIPYWJFXAYPHuzZUymcrvQPpQfoWiZLsSZNmuS8varGFkRKU7JruSyflLqha4r232y3yNWxphb1sYqqxuhHDUVUINa8efNirympDXm6mKJqGIZhGIZhRJLAFFXltUlRvfvuu4u9Ru0mlaMpJVVFHSpmEVOmTOGAAw4IaoihIzVGf2W/pTzNeIbcQuqOlAG1/pswYQIQbSVVKLdYhvmrVq3KK3sj2YZJCdfqNtcKVTro+NOq99RTT/XyBmWxJqVKqrda/C5duhTAs4YDqFGjRhZGnV1U/AH5pZYLRWqkdkshT8fMP0g2btxYTFXSsXXqqacCTn2S0lSlShUv997fFMD/WSqc6tmzZ+SVVFG5cmUv4iQVy4+ac8gOT01KYlGOo9TJXKOIzTPPPONFRP15j7quqzWsrmmyccolsjFTrUkmn6H22fEUVRVD+qN1qg+YOnUqUBiZzWZUZ8CAAUDhOV/3KYka3qheJlZJVeROKrm2cdCYomoYhmEYhmFEkqy1UI0CuWqxptxHtViTy8GMGTO8fA+pWpnk5QY5P38lsaxv4qGcY1mpSAl5/PHHA2mdKsLcfi1btvSUGpl1SwH3u1WERRjzkz1KPONpmYf71cQWLVoA0KhRI+8xKQ6ZqMu5bnHoZ/LkyV5eeRCtVLM9P41Zyr/qATJRh5JR2vkNGDDAU46Ug+qnpNz82NfI1ki2cX379gWCz38Pcvtpv4q1CNNxpt/moIMOAlx+ohRXfztYcFGQM888EyCt1sZh7p+VK1f2cmpPOOEEwKlsynlUO1ip6IrCBkU685OFpKKBylX3Nx+KRXm1mq9ycZM1f9l668LgtRp3tGvXDnBuP7Ix++CDD7yInp+wzy9S9GNbTINryqE8Xl3nwUWPg8ittRaqhmEYhmEYRt5hiupmRJDzmzlzJuBWgC1atPB81eSdp9w4KQRSg2Ugf+utt6a18k9EGNtPeX6TJ0/21J/27dsDMHfu3Ew/PiXCmJ8q9y+66CKvmj8V1NpSzSwy2Z5Bzk+ey1dccQWQXAmVolNQUADgeTM3btw4ECVVZPv8IhVIERq1Kx04cGAo35fK/OSjrSiSqpyFvKVjq60VyfA32pASGXbuXpDbr0GDBoDL805XPZQ3qUz0VdeRDmHvn5qjalEUlVP9hXw4x48fDwR/fg1ifsolVVMU5VT/8ssvnqftTz/9BLj5ZIJUZl1b69Wrl7C1+pZ2/xKLKaqGYRiGYRhGJDFFdTMiyPlpJd+5c2egsCpR/obKrVLFpzqQPPLII4DLpwqasHM4tcJXh7FsE+b+WbVqVa9jkVSDkvIEV6xY4Slh6qSSCUHOT2NWntiCBQu8CnBV80s5Vd6ZVNN+/foBTpUNimyfX+TS0Lt3bwCv3a263QSNnT9TRyr++PHjvdzMkpDKvHLlSq9NbBCdi2z75Tdb2vxiMUXVMAzDMAzDiCSmqG5GhDG/l156CYADDjiAF154AXC5Oc888wyQPU+/MOYntW3SpEnsueeeQPK+6mES9v7ZunVrwPn+qdJU6PFp06YBhfl133zzTWDfH+T8pPRLsapdu7anoIpYtRWcV2EQ+ajxyPb5RfvuAw88ALiOcJvL/LJN2K4iikQpl9jfS13b8d133wVgzJgxQX09YNsv39nS5heL3ahuRtj88hubX35j88tvbH75jc0vv7HQv2EYhmEYhpF32I2qYRiGYRiGEUnsRtUwDMMwDMOIJHajahiGYRiGYUQSu1E1DMMwDMMwIknSqn/DMAzDMAzDyBWmqBqGYRiGYRiRxG5UDcMwDMMwjEiydbInN3dDWZtffmHzy29sfvmNzS+/sfnlN1va/GIxRdUwDMMwDMOIJHajahiGYRiGYUQSu1E1DMMwDMMwIondqBqGYRiGYRiRxG5UDcMwDMMwjEiStOrfMAzDMLJB1apVARg9ejQAXbt2BeDXX38FoH///gBMnjwZgDVr1mR7iIZRjHLlygHwxhtvALDjjjsCsPfee7Nhw4acjWtzImlnqs3d/sDml1/Y/IJh1113BeDzzz8HoHnz5gC8+eabYXydh22//CbM+VWpUoVFixYBUL9+/aSv/eqrrwA45JBDWLt2bVBDyPr222effQDo3bs3ANtvvz0AxxxzDADTpk2jX79+gX2f7Z/BooXV5ZdfDuBtq3Xr1gHQsWNH5s+fH9j3bWnbLxYL/RuGYRiGYRiRxBTVLLLTTjsBsMceewCFK64OHToAsOeeewJOLTjwwAMBtzorDbma31ZbFa53pMzNmTPHe+6TTz4BYMqUKQC8//77ADzzzDNxP+uvv/4i0T6Z6+0XNtlWVL/++msAnn32WQDat28fxtd52PbLLtWrV/eOyZkzZwLwxx9/pP15YcxP546JEydSUFAAwMaNGwF3jlCIv127dgBUq1YNgBkzZniP6T2ZEPb2k2IqBa5v374AVK5cOe7r//nnHw4++GAAPvroo4y/P4j5lS9fHnBzEZUqVaJnz55J31umTOHX16hRA4CmTZty9NFHA/D999+nOpRiZOv40z47bdo0AI4//nigcB8GKFu2LFC4fZW2EgRRO78EjSmqhmEYhmEYRt6RE0W1YsWKADRo0ACAbt26AbD//vsD8OKLLwIwbNgw1q9fX+S9l156KQC33norAK+88goArVu3BpKvrMNckTRu3JjrrrsOcIqVnx122AGAXXbZJeHn/PvvvwDUqlULgFWrVpV6DNlecWnl2KRJE8Btt0y44447+PvvvwG46qqrijyXzyvKunXrAnDcccfRqFEjAObNmwfApEmTgNwpqmLrrcOtrQx7flJqVGwTq+wDLFiwAHDnjKCJyv7ZtGlToFAh79OnD+AUK53v9Xfo0KEAPPjggyV+bhjzUz7ma6+95j02fvx4AM4999wir9X54Oabb/Yeq1OnDgDLly/PdCihbr+yZct616zLLrusyHOa+7BhwwBXRNauXTtPVT7//PMBMirOSWd+e+21F+Cur23btgVc9CzmsxJGwmJf8//jAAqjhYceeigAX3zxRckTKIFsHX89evQA4L777gPgrLPOAmDgwIEAjBs3DoARI0YE+r3ZPr/stttuAJ7qreu8csh/+eUXOnXqpLEA8O233wIuWvDkk0+W+vtMUTUMwzAMwzDyjtAVVeW0NG/e3FtR6rGSqjvfffddjjzySAAuvPBCwK2mq1SpAjgFUhV4fgU2ljBXJA888IC36k2FDz/8EIAXXngBgKeffhpwalsqZHvFVa9ePQCee+45wK2+lZczduxYb7to5VUSFSpU8P6t94pszU/zSGWVf8oppwAu11j/10rzoIMOAmC77bbz3vPdd98BsPvuuwO5V1Rl/zNq1Kgwvjb0+T3xxBNA4n1NSr0U1RkzZnjPTZ06FYAff/wx7e/PtaKqSI1UuEMOOaTYa5Rfp8iTclb33ntvIHmuYBjzUxQmVqHTsRO7fcCd85WvWbt2bS8aJxUrE8LcfjvvvLN3vIurr74acHZcv/32W5Hnb7vtNq644goAevXqBcD999+f9hjSmd+XX34JuHNUIsqUKePVHyTah3QuXLlyJVAYPVu8eHFJQyg1YR9/igTrmv3xxx8DTmVWzupLL70EwN133x3k14c+v5133hmAVq1aATB8+HAAatasmfJnKcKh/N1ly5aV+B5TVA3DMAzDMIy8I7SktMaNGwOFq0JwuUipcMghh3irF6l3fqT+JFNSw+SAAw4AoHPnzt5jv//+e9wxaQUmY+Ann3ySzz77DEituj/XqOLzrrvuApwC+e677wJOEYlVEOQVWBLKWco2nTp14oYbbgCc4ii1SWrNn3/+WeQ9vXv39vKtqlevDjjVQOj/q1evBgq3sxSUsJTLdFE1slSbTCrEs02NGjUSniOEjLlPOOGEIn/Bqa1jx44NaYTBIxVUx5bUxUqVKgGFuYAffPABUKheATz00EPec+DcAIKoug6KRJXSOkfGnlcVxQpCUQ0DKZFLlizxHhsyZAjgzp+luXZJ2cw2upb5UXT08ccfBwqVeqloOtdtrigio/xLRYgbNmwIwHvvvZeTcaWDHCfatGnDoEGDANhvv/2KvEbb9Z577gFcFHvbbbfljDPOAJyTkepWlN+q881xxx2X0ThNUTUMwzAMwzAiSWCKqpQjVcQpPyO2klgr5Z9++glw3ppCK5KWLVt6jyVSSfReVdrnCqlwVapU8RREKTXyEN1c2GabbQC44IILAJfLokpUOTL4c7FS4Z133slkiCmjvKOuXbt6OdPal6VMKXfTT7JK17fffhuA22+/HYBXX30VKFyFliZfJxcox1Gr4nxA7Qofe+wx7/whpAYdfvjhgFvV33nnnUDRc5MqdKWGhN2lKx2koF588cUAnHbaaYBzExFLly4FCpWf66+/HnBOAH4U0ckVb731FlA0R1V+qrFOAInQPitlKJECmCt07qhWrZqXjytFNRHKze/SpYun9PvzV7PFqaeeChTfT+RlK8V+S0DKt78WReeV2rVrA+7cH2V03ZswYQLgtjO4aIuu53pNvEjHAw88ADh/dO0XUl0zyamOxRRVwzAMwzAMI5IEpqhKfZLXnd+Tcfr06Z7aqqo/P6oyi1VU/dx4442A8//766+/Mhh1sCj3dHNTUoX8P+X3JyX1yiuvBNz88wmtks8444y4q8t4SEV4+umnvbzVFStWAE4Rlmrq9z1UNMEIBnVwa9asmfeYfnOdiz799NMif6V4PPfcc573qs5fqrIurUtFNlEOmfLCpHxI7VK+qaI8v/32GyeffDJQ3M9Q1dY6n+YK5fjHot9eanCynEf5TUdVUVWu3syZM73tVhLKNd511109f9tcKfxywVD9gaIWyslX9EUK2paI8jP1GwTRRSwspKRKAdXY161bx7333gvAyJEjAfj555+TftbWW2/t+aVKSRWKusqJJVMCL6aSAbNQuKOgoCDhznzeeecB7iIRi34sGVfLvinXN6iyGTrqqKO8x2QxlQilBHz++eeAa5eaD5QrV86zUxFKKleSdT7TsmVL7wZVoX/ZN+kGfXMvEshHdCMQiwpXZMjtR4uKeMyePTuYgYWAQuEK4yvspkW7n/Lly3uFjRIOZEul8HOuz6MXXXQRUNj8RYVHjz32GFC6400pNYnEj1yjFq+p0LFjR+/fqRimh4Fu/JXC9OijjwJujLphjff7K1VFN0fx0CIrV8XQQfLPP/8ApbNiyjZKkZk+fTrgblDFVVddlfB8mYghQ4YUu2e75ZZbAGf3FxQW+jcMwzAMwzAiSbg9E3GWPslCAwrx+NMFPvjgA1q0aAFkZsQdBrK6iTXDlZm2mhJ06NABgJ122glwhtVSMQYOHOjZqiSyZIkKlSpV8tRybVNZj+UzssO67777vFCqLIqkIK9ZsyY3gzNKpGfPnt6/FfLXqj4RTz31FOBaroIz6ZbiEEWkoOpvImSXM2rUKK/wQ/u20gKUJpBr1HLxlltu8WxxZKXlR+fR2OKxoJWbXDJmzBgA/vOf/wCFloclRemyhawVE1G+fHlP6ZfaqmI/Xff8LVTBtTWWcitLPCntUbJNS8QRRxyR6yEkRPcpUksVHZTVm5oVzJ8/v8TPkm3j4MGDARcJB7fdZFcWdAqOKaqGYRiGYRhGJAlMUdUdtZK/u3fvDjgLo2eeecZrA6e8U+Wv+vNalTjerFmzYibrUaa0ZuFqEzpy5EivzeG5554b1rACR7k4yr+aO3duLoeTFlJS1f51hx128FbxQVlq5CsqZsx1flxpUD5jrVq1PNVALUT9yPZn//33L/bcNddcA2wexW7Km1fb6Vi0v0eN0uTHqU5BeZEQ7Zzi0nL66acD7hogVT8f1OJzzjkHgLPPPttTghOha7mu+23atPFapAuprrou6reJol2cUP7nnDlzcjyS4nTp0q1dBksAACAASURBVAXAK6oUKpxKZgGn86WKAC+77DIAz8YxFkUDwrIrM0XVMAzDMAzDiCRlEhmWA5QpUybxkwmQTYFWhbFG06tWrQLgm2++AdyqSWgssicJWtHZtGlTkf6W6cwv5r2Ay3dTrkcsv/zyCwCLFi0C3G/SpEkToFCRVNVq69at0x2KR5Dz81OxYkWvTezDDz8MOBNqKXALFy4M6uviEsT82rdvDzgFR3mKH330kdfmN1f5wmFuv1jkWCFlQ5YzyrFWs45LLrkk0O/N1vz8KIKhPNTYnDLts4oOZFI9HsT8ZOuivD5tm0mTJjFjxoyk71X+2csvvww4yyZwea2KaqVDrrafUDttKXdLly71mjnIGD8Tsj0/NauQ9ZOunbo+6LoRFJnMT81elDOrMSqSWrlyZe/6rWua1NCY7wdczr9acQMcdthhgIuG6LVqXnHttdcC8Pzzz4cyv3SoW7cu4PJ3NUY5JARNOvNTq1t/df6JJ54IFFVUjz/+eMBdI/V/zTPBmADnvpJJTrF/frGYomoYhmEYhmFEksCr/rVauvzyywFXGd68eXMvtyg2xygW3Z3LxHvZsmWeJ2LU0FhVobp8+XLPd1NKlXwA9bhQPsfQoUO9doizZs0CXE5v1Fi/fj2TJ08GnAebDMiVl6wK42y3QU0F5X35Iwlz586NvPNCUMiBQ5Wf+i3ksZksypKPaD9t3Lgx4Oa3ePFiLxKiaE+2kMOJVBiNUfgrpM844wzvOOvXrx/g3EOkfOuzlFu2YcMG7/yi9+YjUsD97bTHjRsXiJKaK7TN1bRAOapBK6lBoN9Zf7V/SrXfaqutPH9wNSwoKToR+7xcKLbddlvA5VIryqXfZvbs2ZFpLqD8WlXWy1c8SiQ6PlJxk1ADA20vtYz966+/6N27NxC+O4MpqoZhGIZhGEYkCTxH1Y9a5A0YMICuXbum9N6NGzd6lWZ33XVXpkPJeY6Vn3LlyvH6668DeB6CBQUFQGorHpGt+e21116Aqy6WF6xax5555pkAgavhQcxv4sSJgKv83meffYDCVoB6TnlSqmBU6025HYRFtraf/PD8Sr9UEnUau/TSSwP93mwff1JnFNlQDq6U4/HjxxfxAsyUVOYnL0KpoEKtTZVbpm0QWx2t9odyT4ltHwuuBmD27NlFfGbjoeiWKnmTVVeHsf2UV9utWzeaN28OODVNOcVq2axriSIB++67L999912mQ/DI1v6pnH7VLEgZ1zUgyDnFEsT8FPmTv7n4999/Pd/wIDx61dVKOeTK2e7evTuPPPJI3Pdk+/wiBVnbK7YeJwzSmZ+u0arJiNey2I8UVPlRqw5Hf5W7+vXXX7PnnnuWbvClwHJUDcMwDMMwjLwj9M5UUqXuvvvulBXVrbbaigEDBgDOLzHbuWRh8tdff3mVvPL1lPdhVDqSxOOLL74Aiq/8paxKBZfHXpSQr5zQirNOnTqMHj26yGsUbVCetTpV5TvKNXriiSeA4tW5+Y7UlylTpgCJc3Bz6c2o/ESNRT3PlfMlpUIqcCwHHXRQkff6kUq8YcMGz0FFqFpbbgLq8qTPkqoXNGXLlgXwrgFSilXBr25a4NS0vn37xv0seUCGpTyGharcpaTKjULbPB/m44/CiMWLFwfa7UzHg45RKbiDBg1KqKhmG0UW1Y0piqjjlN/ZRTm/uhbE+ivrPKlcYOW3S0kVJXXIC5LQb1RFPPsmtfPTBVPIYHbQoEGema6StjenG1WAt956K9dDSBtZk6hAqUePHoALU+66666sWLEiN4MrJTqQ58+f74W1dGOqg1mNADTPKBY7pIIS7BUS39yQ/Zb/xCp0kxSlAiPdnKkQUwVR6RS2KUS3zTbbULFixSLPKb1DN0U33ngj4FJiwmLo0KEAXHXVVUUeV9j7yiuv9Jot3HnnnYBL3fDz0EMPpfz9CnmGZUheEpUrV/aslXSDqvNNlEUJP7Kui11YAPTv3z8r3+9vDpQLVOClY1PHW5TRTaf+luYmU2lBKp4SSi1ScXU2sNC/YRiGYRiGEUmypqjGqhtKhtcduYpwhNri+S1bNkcUZpbSIZUyn9CYpQxodd2+fXuvMCcfkMK4fv36Io9XqlQJcErc2Wefnd2BZZlTTz0VKIx45GMEI1HIWOFENazIpc2NCod0jvO3JZRVmlSbiRMn8sMPP8T9rNNOOw1wNkd+aytwRVoK8WUrbFe7dm2guOH4l19+CTg7vi+++MJLO9iwYUPSzzz44IO99yRCjTyuv/56wDVSeOKJJwItoCstl19+udecQGgcP/74Y9bHky5SpP0tvytXrszw4cMBp3pubilFQgrj77//DsBXX32Vu8GEgM4fOmZkRylkybls2bKsjckUVcMwDMMwDCOShK6oKvdKJv7glA21xBO6k5dhcD6w8847A4X2WcoHk2JTUu7K/vvv77UyU7tVv/KQLWSqrZVyJq0WpUAqF2tzQbZVmztSwfx5aFFHBRd+xVsr/xEjRgDFFfNcoDaT+psJo0aNyvgzwkL53mrBKVSUc+yxxwKFRaRS4GrWrJn0M1XT0KlTJ0/VEioakULtz9HNdlMP5eZec8013vVN6naDBg3ivkd1Gc2bN/esgqSARzXCsW7dOq8I5+abb077c1R0p31A56AoRRzVYnT58uWAu94pp1OtxTNpx5xLdOzcdNNNRR5Xo6IJEyZkfUymqBqGYRiGYRiRJGuKqixjILGdg6qrY/Nf3n33XQCvIjRqSM3o2LGj99jLL7+c9D077bQTUJgDKEVWVbi5WoUpl0tm1CeccAIA7dq1A+JXy2rVqyYFyrf99ttvgfzKvYqlT58+gLPpUE619sXNhbVr1+Z6CIFRpUoVBg4cCBRv0awIRzrV4kZmyH5PtljKo1WTAn+zAnC5f1KudL6UfZwcEfzWW/FQXq/cBrJlJaSxx1raKWf4kEMOAVzVf2mQ08icOXOCGmKg/Pjjj15esK7fain68ccfA/Htt3R/IOVbEUa/KqvfTk5B2aZRo0beNVEOEooSqCGM1F9FbJo1a+ap5/mAcov9LhR//vkn4JRURX+ziSmqhmEYhmEYRiQJXVHV6vjff//18k/OP/98wN2hn3XWWYBr2SX+/PNP77VRyCuLR7w8PuVaSR2VMqeqVuXgSikAVwWbK+6//37ArWhV6bdgwQIgfiWutqff71A5gSUpy1GjX79+QHFjeHlNZrPKMRskamQgb8t0PDxzxU033eSpdkLbMap5fVsCatTyxhtvAIXV7+By+2OZO3cu4PyK/VE0bU9dE/bZZx8vn1qvVc6vWtRK7cq2w4PUexms//vvv94YlW8pZUqKlVrGqtlDzZo1vYYkOiajSqtWrXj22WcBp5pLmVM+slwaYs8rjRs3BlzE1e9YoWP30UcfBZzHbtjIWeL2228HCiMCut75kce79k853eTKszdd5A+7++67F3lcCnkuG6SYomoYhmEYhmFEkjLJVJMyZcoEJql8//33xVbR33//PeBWYH6GDh3KddddF9QQ2LRpU5nY/wcxP1XH33HHHWy9deoCtTojyXs0k1VLJvMrV64c4Dw0U+lUo9wjrXrV4k5qRlAEsf20IpcjgcbesmVLr42tVvXqGiavR3+FcdCEsX8mQ9EAeWvKH1CKgHxjgyKM+e2///5AYb6f/zyi3FS1JQ6bbG+/bGPzKz2KvlSrVg0oVIGffvrplD6jatWqnotBEO4QYW8/jfWSSy4p8rjqN1Qdn+yeQ5E7tT/WtUT5rsmw/TN9qlSp4u1jUpNVY6JIlaKscmsKuuWvf36xmKJqGIZhGIZhRJKsKapt27YttQ/ltddeC8CwYcO8vI8gCHNFcsEFF9C5c2fAdZ0picGDB3PvvfcCwVTSBTE/qYlt2rQB4LDDDivxPfI19HcYC5og5qdcNf9+v2bNGi9HTqq28pOyhSkCqaOOd7GVqlIClG+drVwx2375jc0vHOQOoxqAWOQIoKiOzsvpuPzY9kufq666qphv6uuvvw64qJWu837FPCiSKapZu1GtXr261zLVfyOnhGn9UGEZWNuOnN8EMb+ePXsC7uAT06ZNY+nSpUBu7DfAtl861K1bFyi8Ua1Xrx7gWlNm25jatl9+Y/PLb2x+6RPvRlVIgJKVXFgWmhb6NwzDMAzDMPKOrCmqUcBWXPmNzS+/sfnlNza//Mbml99safOLxRRVwzAMwzAMI5LYjaphGIZhGIYRSexG1TAMwzAMw4gkSXNUDcMwDMMwDCNXmKJqGIZhGIZhRBK7UTUMwzAMwzAiSdLm9Ju7/YHNL7+w+eU3Nr/8xuaX39j88pstbX6xmKJqGIZhGIZhRBK7UTUMwzAMwzAiid2oGoZhGIZhGJHEblQNwzAMwzCMSJK0mMowDCPfaNSoEQCnnXYaAP379wdg48aNANx8883e37/++guAf//9N9vDNAzDMEpBUsP/sKrKtt12WwAGDRoEQIUKFYo8X6lSJQB23XVXnn76aQAmTZoEwC+//JL2925pVXPZml/Xrl0B6NevHwBr164FYODAgbzxxhuBfU9J86tbty4AX331VUbf07RpUwA6dOgAQI0aNQA45phjANh9990BeOCBBxgxYgQAn332WUbfCbZ/Zsqee+4J4G2TU045Rd+j7wfg+++/B+B///uf9+/nnnsOgP/+979pf79tv/zG5pff2PzyG6v6NwzDMAzDMPKOrCqqe+21FwDTpk0D4MADDyz1ez///HMApk+fDsCVV16Z8vdvaSuSsOYnRfGmm24CoFWrVgA89dRTgFPMmzdvTvPmzQH44IMPMv7eMOd36qmnctVVVwFw6KGHAlC2bNkS3/fSSy8B0L59ewDWrVuX9hhs/0yfqlWr8vLLLwNwyCGHFHnOr6jG4++//wagT58+ADz00EMpjyGM+e24444ALFq0iCeffBKAvn37ZvqxaZGr/fO8884D4MgjjwSgXLlyABxxxBHsu+++RV57/vnnAzBu3LiUvyfb86tTpw4A3bt3B+CAAw4AoF27dsVeq337oosuAuCTTz5J+fvs/JIZxx13HACvvPJKyu/RX0Xr9H8xZMgQAAYPHpzws7a07ReLKaqGYRiGYRhGJMmqoqqV8cMPP5z0dSp62Gqr4vfRf/75JwAvvvgiUKiExb4nGWEqHscee6y3uq9Xrx4AO+ywA+By5fx88sknvPnmmwD88MMPQGFeZ7pka8U1ZswYwBWrnHzyyQDF8lG/+uorZs2aBUDPnj0z/t4g57f99tsDcOeddwJwxhlnePubtoVyXRUBmDdvHgAjR44EoHHjxt7nNWzYEIAlS5akO6RA57fbbrsBUFBQAMCKFSu8f9euXRtwClUiFixYwOWXXw7g7aeZEOb+WbFiRWbPng0Un9fYsWMBGD16dLH3SamqXr06AEuXLgXc9kyFMOanyNHw4cM9JUdRimyTrfOLIm/KEde5QwpkMh5//HEAzjrrrJS/N+z5Kcf9mmuuAVzkRvteafjwww+B1KKRYktT5DKZ39y5c4Hiymc8dFy++uqrRR5v2rRpqd4fi6I/8djStl8spqgahmEYhmEYkSSr9lRNmjRJ+rwqb2Uf07JlS29VvfPOOwOuAlsqnvIIS6OopoJWu1LcXnjhBcCt1KW+Va5cGXDKairsu+++ngr7888/A071+fbbb9MdemhIEVBOlVwbklX2b711tBzQtN2kasixAFzFt1Ssn376Ke5n3HbbbQBeziA4hSMTRTUdLrvssiJ/paQGwZFHHsmCBQsAl5e8fPnywD4/SNavX8/48eMBmD9/PgDvvPMOAFOmTEn4vn/++Sf8wWVAbL6i8vPDQE4ru+yyi/eYtvWGDRtC+95Ybr31VsDlYfrdYEqDFPKoIBW4R48eXm5xOvMSyk1t1qwZ4K5H5cuXBwqjlrJcM1InFSVV+PNQ0yGVvNdMkGJ70EEHAYX7JUCvXr28HH5/Tr8iiDfeeCMAa9asycpYYzFF1TAMwzAMw4gkWctRPeCAA5gzZw7g1FEhBfWWW24B4Ndffy32fq0A7r33XsCpe6oAVdVuMlLJ8QhaoS0JzVlK7hdffJHyZ4Sdw9K5c2cAJk6cCMDBBx8MwHvvvRf39V999RVHHXUUAN99913G3x/E/CpWrAjA77//XuTxhQsXehWZJSlI2k9lJA9wwgknAM4FIB1SmZ/yY6V4JkJq4tSpU73HvvnmGyBx3qlU2TfeeMP7t9Q1KavpELUcq4oVK3p5yIqIKA/52GOPTfnzwpif9tOKFSt6Y1T0JQikxCk3ftCgQZ5XtRonfP3110A481NEqVmzZlx33XVAetEpofOMPiuV6v8g56frklR9VfQn49NPPwWce0os//nPf4BCb3Fw1wm/+lW5cmXWr18f9/OzffzJ/aVTp04APPjggwAsW7YMKIywBnFdEEHML9n9UJhkq+pfdQrypY/5LFauXAm448+vsCrCqDHed999qX59UixH1TAMwzAMw8g7spZA2L59+2JKqnL95Mf5xx9/JHy/VLsWLVoALh8k28qnUA6p1AbAyw2S4ihq1aoFOBVYc4hFanM6Smq20AprxYoVgFMAkrFq1apQx5QqLVu2jPv4ueeeW6KSqnzoww8/3HtM+1+QKldp8Fe2K4/orbfeAmDy5Mlpf7bU0379+nkr7yBzX3NN1apVgcKcZFVcazvef//9ORtXLIpWbLPNNkChApJJVz4/UkuVd1a/fn2gMCf2tddeA7KTi6a8W31/pijHVlGPdPxUg0CqUzwlVd3QlE+raMezzz4LFL2mXXzxxYDLRZWiKnROfuyxxwAikZ+qaI+6Sqou4JJLLgHg+uuvB2DChAleJCpX13Gh3NREDBkyxNummeSk+nNRs5WbKlTLoNz822+/HYjvNrTffvsB8MgjjwBOxVdUu3Pnzl6tkD9CGTSmqBqGYRiGYRiRJPQcVXUtmjZtmpcfqJW6enOvXr06068pFankeCh3Rr+Pqv7lqan8vnhKmqo61eVG3VL8XVQA7rnnHgDPrzKTKuSwc5CUayQf3P333x9wOY9+CgoKvMr4IHJ/gpiflOvjjz++yOPVq1cvcT/U6lOrUnAenVHzic2EeDmqMWNK+3OjMr8LLrgAKJpjJf9UKQRSvVIhyPnJUeOGG24ACqMwmeQ/C3VQkxeycuOlFkmZi0cY209OG127dvXOIzqmnnjiCcBVJsuHW3653377rbe9dC7Sefmzzz4DnEJeGh/gIOYn9wRdF2Ir/NeuXQu4/OdE3fp0jPXr18+r30jUJU9OF/qNkkWFwj7+unXrBjiVfvjw4YCLMErtVUSuQoUKnmtOorzaVMhkfiVdnzI57wVFJvNr3bo14PKf3377baB0ufjKt5aqr32yRo0aXhRA55VMrvPJclRDD/0PGDAAcEUsAB9//DFQfOesW7cu4G4ScxnGiLVpKS2yktFFxt/KMR577LEHAB07dgRcCCSZpU6u0clFKQ2JblRjC3iigi5Y/hvVZCjUoYuBmDdvnrd/bw7E3qDG/h9cc4d8QjcvOhlr++mYi+Xaa68F0rtBDRLdkLRt2xbIfIGnz9ONqNoE6+ZB+2+QRS2pENu84MwzzwRcOtW5554LuJs/pdyoTfGsWbO8bazQo7ZjSaHcsNANTTwLKokQJaUJyaLQf74B1/BGAofmm+uQf6dOnbwFhhYSsnZU+sqIESMA99s89dRTWbM+S0Sy4iVwRU75jkQzbQulW5YG7VtqK61zxVNPPeU1M9JxHMRiOh4W+jcMwzAMwzAiSeCKqlaUMoU/+uiji71GhSAqSJJqIKuU2BCBlEZZWuTKPsKPFFeFozp06ECDBg2A+K1fE9GmTZsifzU/JTBfccUVgAvV5RJ/izhZXaiAx88JJ5zgNXnQakx2QLlCq3oZ/os333zT2y+l4KggQ/PUdpWFUdu2bb1wXj6TrGmAfpMgWqgGiRL7FXpauXIlEyZMAODss88GnJWWiqfinTtke/fRRx+FO+BSosYRml8mNGvWjH79+gEu9Hf66acDxe1pcoUKirp27eqdI3R8qZWq0oekmuqc26hRI09dVZpArpRUoYJgzUXhcHCtUpWOoHn8+++/ADz//PNA0WJNoblLAVQaUq7ZbrvtgMLrkwqDpISr2YuiV126dCny3okTJ+a8iKokZFk4ePBgrwisJPQ7DBkyJOvFUqXl9ddfT/u92k9HjhzpRWikoku5Ddq6yhRVwzAMwzAMI5IErqjWrl0bcAnxydAKM9nzn3/+OeCsLZTsH6RVSzqcc845AAwdOjTQz5UirZzeO+64A4D3338fSN6uNGz0m3/44YeAU670G0gJ15i7devmraxylQPnR4qH9iPlxdWvX98r7FDem3KmpcRpJXnGGWcA5K2aKjVGBXyJiH3eb/yfK3TOUO6YLO923HFHTwH3IzVKed/ajlHMofZTUnOGeMj+7vbbb/fyyGUDFDWFJ9aOb++99wbghx9+AFxhm3IapdSp1Sq4eoC77ror9LGWBp0rHn/8caCooiqUf6xmAMpdlYIcixrZSAmfMWNGwCPODEWb1qxZ420fWY2p6EZNX3Tsar7+CF0USceKKvY9pTHyzwa6r9DfdOykVJui/VTbNfZz4xWMB4EpqoZhGIZhGEYkCVxRVeu6VJBx/D777JPwNVox6y5eK8xcUa9evRJfIxsu5WFJjdL/Y1G7uQ4dOgDO7kHWELK4yqWiqpXw1VdfDTirrrvvvhuAE088EXArr3bt2jFz5sxsDzMpUiiUSyMV+JxzzvHyhP2okjHR81FGbW8LCgo8e7HSoiYCsahla+xqOpu8+OKLgMvlVI5bbP6pbMZ0vHXt2hWITh5qKkipSGRPFIvOiVJvfvjhB++xqM5dkbf69et70Q2Z5M+aNQtweZCKdIg5c+YktdPKJVILZQN02GGHFXuNIjaJ+Prrr+nduzfgogBRpVy5cp6KrOu4rJBUEa7rnmwp1bJzc0Z5rf781mwrrTo/plPjo+u5FHLV5zz55JOeSq7mAGFhiqphGIZhGIYRSQIz/NfqcNGiRQDssMMORZ5/5513eOaZZwCXK6ZcJHmpqepf+ZmDBg3y1BBV7qpCUq3lVA1fmpVCkIbHqtiUMvfmm296yrAUOFW4p5IPIuXkk08+AZxyK+Uvnj+fyLahulotqjWsPCilrCYytE6XMOe3atUqtt9++0TfC7j9Vs4BixcvDrRqNYj5SUHVGNNpfRrPw9evxqbjBhDE/N59913AKapSyAcMGOD5M2s/zGT/k9uImgOounXJkiUJ3xPE/LS9lL9Ys2ZNoDDH1O9RqPPllVdeCbhIx3PPPQcUGsYH6bIRxvGnVq7Dhg0r5rqRCDXt6Nq1a6Cti8OYn66LL7/8cokKqtA55ZhjjgnUbSOM+Slf8corr/TqU3RN1vVe8/7yyy8BPHcO1XkERTrzk0tESTmor7zyiqeClpTnrc+6/vrrS/zcZs2aleozIbPtp/OXVFE1Y4jXOlXnnJ9++inpZ+63337euGvUqAG46Oqll15a2qF5JDP8N0XVMAzDMAzDiCSBt1DVCr5OnTpFHr/nnns8FTQVlM/Sv39/oHirSnVGkJ9iMqLSwjEZ8k9cuHBhkcdVaS8lKR7Zmp9ykOSdJhXo6aefBlx+bdBkS1HVMaGcXHXz8HPNNdd4FchS+jMhiPklOp4XLFjg+Rb7q90nT56c8udKdZWCW8qxpT2/xo0bA05NU9Tlt99+AwpdQaSkKo81HVR5ruOvSpUqgHNKSNaJLMj9Ux2HpOJ88cUXXmvK6dOnA64TntwqlDOubRK0R2WYx1/16tU91wZFq/zIM1URm6C9fcOc3/Dhwz3luyR0LqlduzY//vhjUEPI2fVP1zSpl+rYGOv4EATpzE85ook8UlNRPOMhRbUkf9/SfE8m20+1Ln6PYl2rU3FgUM7q7NmzvSiIrg/qmGaKqmEYhmEYhrFFEHjVfyLkh5oqymvRHbpy1NTdQz56pVFUo4wUI/88lN9a2tV4mGhVJrVHnUe0bU899VTAraCVZ5dvqJJY3dWkpknB2nXXXYHCfsmqUL7ooosA5/SQK0477TTAVeeH5XuabT9VKQ5SUoXcMsaNG+flcWv/S6XSXSrBhRdeCDglNVdIPT344IOBQjcQKaeKrvh9N6Uwah8oDX6P0ljkNKKugGGgGoS+fft6Slsili5dCkSvS1oy5FiQimOI6hTatm3Lgw8+GMq4sok8fFWLErSSmgn+qnspmkH5Dcd2qYLEyq2U17B8jnVuPOKIIwDnx6to2qhRo7xIm3/7VKtWDXCdC3VemDVrlvfas846K5Rxi8BvVBWK86N2humiH9p/wysLk3xkq6228g5i3Rz5i9AeffRRIBr2JLoRlY2MDOF1Q6q2bLrg5MONasuWLQF3MIK7CdM+p5QVtbPVATtkyBDP/kcFAzL4VjFctilNGD+Iz4tXcBUGakusGzc/amu7ceNGr7hILRt1o6oLv4oElJbUsWNHevXqBbgbU3+4/M8//wRyZ5Svm84DDzzQK5CT3Y+fWCP8kpA10Pr16wF44YUXgMLULaVOhNnuWOcQCQ6xRaIK8WvRpyIdmYnr5j1ZYVtU0MXfb61VGoYNG+ad91esWBHouLKJrIvSMZnPFmHbRJWUYqBWrWGjEL1C/q1atQKgR48eXltxiWUqltb5VOcDLeaff/55z35SnxtkcWMsFvo3DMMwDMMwIkngiqpaqsncV+awp5xyCv/73/8Apx6mEiaVWtKwYcMij0tyTqfRQK5o0qQJUGgNIUVPaGUybtw4IL2k5LCQFZfkfm1H2Vhom6s5w8yZMyPTOjURn332GVDYWlXh38WLF8d9rRRWFX1stdVWXuqJksqlgKuNZa5TATKhc+fOxWyppDZnK/zaunVroHgxl/Y5RVRiQ09qt6wUAKULqJlGPKSkqsWuirakYs6fPz+DWaSPpSknDAAAIABJREFUCmuWLFnijUGKqhTUdFpRKtpRkgVNWKjNcqySKsXtiSeeAOCGG24A3HlFrTkV0ejVq5e3vaKCGjQo+hLPnkgWjlKwEkUFa9SokfMUlEyQGbx+AzUAiBL+FqlBh/5THUe20LVMFmL6Gw/ZjcXD35o1rCiqKaqGYRiGYRhGJAlcUZXpvZJ1lc9Qv359z7qgX79+gFu1vPXWW4Az6FaBSizK4ejSpUuRx6VgRZmjjz4acFZaUnykEseiggkV8kSFo48+2lMNlavqV2OkEEj5adu2rZeDFlWUd/PTTz95iurQoUMBPGsY5cIpd04MGzbM24ZS9KX4y1DZb6eWD6iwb9KkSd5jUlK1L4eN8hBVpOZHatvDDz8MFBr/K8l/jz32KPJXq/141l1SI5WXKRuZKBbsSBnWGHW8BWGNli2kEEolVQQO3P7mP2bUyOC2224DXAHW6tWr6du3b7gDThE1K4iXLyy1SYVVyRR+SK/dZZSoVasW4KKqynmMAolyRhPlkA4ZMiSjPNbSNBTIR/ytWdV0JWhMUTUMwzAMwzAiSWj2VL179wacYjV06FCv+lYV0rJE0V/dlUsBSYbcBWQyn2sqVqzo5VBJ9VVl68477wzEN45XNefIkSMBuPfee0Mfazocc8wxnllwIgcCf5VwIqP8KNKhQweefPJJwFUXSwGQPY+qyJXXWq1aNVavXh3386TO5hMyik+mpGbLluqMM84AnCojFKlRfrQsby699FLP8F9WVkcddRTgco6Vbyp7J3DuDGruEFUaN27suU+oTWE+KalCkaJYJVVIMfWj5gtqlyukykaZWDVflf86j/gdXvzMmTMnUlZOqaIIqY43Xf+jQCLlNNnr9R6pn6nkhpf0fenkmUcBf45qWE4hpqgahmEYhmEYkSQ0RVWr/WHDhgHw9ttve56Tqoj2e6smU1JXrVoFOPVVd+5atQWNckVlqq12oVKD1WpRVKlSJWl7U3CVxTJjnzZtGhMmTADC8x8LilWrVpVYIazcRpErL9F0WLJkCQcddBDgGhqo4l2+m/Xq1Svxc1RNqf0lyqj1rRoa+Cv8wams2Tb415hU9a/cP/me/vrrr0Vev379ek+1UH5w1apVAVdNHmUfx5KoXbu213BDxvf5iPLXVa2v7QqFLiiAF6VQfrucQ+QGoO3cvHnzpNXKUSA2z1Q5xv6mFX507TznnHPyUjUX8r9Vg5Rsn0OSoaiL9qVUqu79TgGZIHU2bB/XsPDnqIaFKaqGYRiGYRhGJCmT7E64TJkygd4myzNPbQ/VDUjdmZQbqBXnVltt5akjyiFTS9V02LRpUxHJNtn8/B1q0kF5tMrtVA5WWBV+qcwvVZo0aeLlByr3T2q2ugcpX1jbtX79+oH6NIY5v2RIKfZXoB966KFejuT7778PwCOPPALASy+9lPL3BDk/eU3KqWHq1KmesqHH4imo4PKl77jjjkBVkFxtv2wR5vwKCgoYO3Ys4I63bCtUQcyvUaNGgIs4xHaES5X27dsHqqgGMT9du3Ru3HPPPfXZCd+jSKL8Km+++WYg+O2b7eNP7j3vvfceUFjnAOF5Egcxv0Rqaao5rSWhewApu6UhiufP5557DnAdrtQpMB3884vFFFXDMAzDMAwjkmRVUc01qaxIUs292LBhg6f2Tpw4EXDdpb799ts0Rps6UVxxBYnNr2Sk/ioPOhWktIblIWrbL30KCgoYNWoU4HKLs00Y81PnvSpVqnj/VkejkoiioioUNVTFu6IZgNehcdq0aQBMmTIFKKzj+P9xpPu1Scn28ac6DvnHqiOjfKmDJlvzk9paUn7rK6+8UqyaP5Nc1CieP2fOnAk4RTUTZ4dkiqrdqCZAB1XHjh0B+OGHHwB3ApJtyOeffw4UFneEdQCWlijuyEFi8ys9Cq/p5nP58uXeDY4ujFOnTgVg8uTJ6X5NStj2y29sfvlNtudXs2ZNwFlVDhkyJMyvs+2XA/yhf6V31qhRAyClFuoW+jcMwzAMwzDyjtDsqfIdmb1Hqe2bYZQWFbwZhmHkgp122glwFo/G5oc/RfLTTz8FXAMD2YlmiimqhmEYhmEYRiQxRdUwDMMwjEBZu3Yt4NoeG5sfbdq0ycr3mKJqGIZhGIZhRJKkVf+GYRiGYRiGkStMUTUMwzAMwzAiid2oGoZhGIZhGJEkaTFVFAxlgySKhrlBYvPLb2x++Y3NL7+x+eU3Nr/8xgz/DcMwDMMwjLzDblQNwzAMwzCMSGI3qoZhGIZhGEYksRtVwzAMwzAMI5LYjaphGIZhGIYRSexGNSAuvPBCVq5cycqVK+nbty99+/bN9ZAMwzAMwzDymqT2VNlC3bEmTZoEQJcuXXI5nLSpXr06ACNGjADgjz/+AKB9+/YA1KhRo8jrb775Zp566qksjtAwDDF79mzWrFkDwOmnnw7Av//+m8shGUZeoOPl8MMPBwqv3bVq1QJg6dKlABx//PEANG7cGIAZM2YUef7bb7/lr7/+yt6gjbzFFFXDMAzDMAwjkpSRmhn3ySwZykrFkAL54osvAk6JDIowDXMvvPBCxowZo+/R55f4/2nTpgHQq1cvAH766ae0xxC2IbC2R7t27Yo8fvXVVwPw3XffFXm8UaNGHHDAAQA0b94cgJNPPhmA7bffXmPU2L33nXjiiQDMmTOnyOdtaYbHNr9wqFixIlB4ntG5p2XLlgD8+eefaX9utua32267AbDddtsBcNlllwFOuRJvvvmmp1jtsMMOALz88ssA3nknlfNNVLZfWNj8SqZbt24AjB49GoCyZcuW+r3+c/3o0aMZOXIkAF999VWqQymGbb/cUb58eQB23HHHIo8rBfL7778H4O6772bDhg1xP8MM/w3DMAzDMIy8I1KKqsbywQcfAIU5LqtWrQrse8JYkdSsWROA5557joYNG+p79PlF/j9v3rwi7z322GO95959910AWrduDcDPP/+c8ljCmN+ee+4JwKOPPsphhx0GwFZbFV3fXHvttYBTqgoKCgDYY489vJVWov0snqI6depUAE477bQir43yijIIbH7ZoUGDBgAsXryYRx99FIBzzjkn488Nc341atTwcvdbtGgBQNu2bfU9KX+elOOLLroIgHHjxpX4nlxtv2222QaA/fbbr8hfKcnJGDBgAABz584t8bVhz69Ro0YADB8+HHA5nH60PSdPnswTTzwBwPTp0zP+/kzmd8QRRwBOkdd5PRXinesHDRoEwLBhw1L+PD9hbr9y5cp554j7778fcNtkyJAhgMu9DYuonD9Fo0aNvGPz7LPPBgojy7Fom3/66adA4T1Pons6U1QNwzAMwzCMvCOSiqro0qWLp64FQRgrkieffBIozNv0rxiXL18OwKGHHgo4lXTfffcF4Mwzz2TgwIEaC+Byx6RKpkKQ81PO3uTJkwHYdtttU1JFS/Nc7PPKpfvll18YO3YsAIMHDy7y2lytKOvWrQtAq1atAOjfvz9QqBh/+OGHgNsP/GNOhWzPT/PSPti9e3d9r8bjvXbChAlAZspjVBQBKaozZszg1FNPBQrV1UwJM2Lz0EMPefndYu3atUBhNAfgk08+AVzkZvfdd/deK/WuTZs2gMsRX79+PVC4LwOsXLky4Vhytf2knN5+++0pv1fz6dChAwDz589P+Now59egQQPP4UXHnSKH//3vfwG3jfbee2+gcJts3LgRgIULFwJw9NFHpz2GTOansb3wwgspf6/2sUqVKmkc3nN///034LZLIpW5NIS5/YYPH+6d9/3IOUSq4syZM4P62iLk+vypGpV+/foBcMghh5Q6Yqrte+yxxyb8fFNUDcMwDMMwjLzDFNU00epC49u0aZO3evjoo48A6NSpE+CUDj+VKlVi/PjxRT5Pv4Hem4rPahDzU/6b5lW1alV9VkaK6i+//AI4Lz3x2GOPAfDbb78BhZXKicjWilJKVMeOHQGnklapUgWAdevWAbBs2TLq168POLVAvoKLFi1K+XuzNT+posoPk5pWGv6vvTMPtGps3//nlAZRytxgeMkUMhNRhkJRGdOr0CAyRaEUfUOJQmUoQ+nNPCQikbzRSJThzRRlKGXMEJFC5/fH/l3r2Xudvc/Z+5y19l6r7s8/+5w9Ps9ea6+1nuu+7+uWO4VU71wotCKgilSp4Pfee6/3HQRBGPNbuHAhAPvss4+nlGq//P333wHnlpINUlJ1XGnWrBkA99xzD+ByVtORr+23//77A871Q44F5cnFFePGjQNc1CAdYcxPUZgxY8ZQr149wClSyo/2Oy/omHvggQfSp0+flPfRMUgqZS5UZH7bbbcd4PYPKZ8NGzYEEh7iUn/nz58PuH1MeYyDBw/WOEq8/5tvvgkUTjHOhJw0ZsyY4c1D+ZaPPfYYANdeey3gzguqM1mwYEFFPz6FMH9/NWvW9LaXzmmKMul3p22uWpTkx7RNP/30U8CpyrNnzwbceT2XiE0ypqgahmEYhmEYkaSgnamaN28OuCpyrcjigKrbklf5WoFkW7n/xx9/eBXzyqESqmzNV+cq5aROnTo143Pkk1q/fv20j+u7kCfevffe630n8saNKpUrV/aqquX9JhcHrQqfeOIJINHRCODzzz/nmGOOAdz87r77bsDlsfqdHvKN8qF79+7tuShI/dXvTvm1yoOTH/Duu+/uqSPC7/gQJ6SMq4PcrFmzCjia0lGF+K677urdp25AFfFa3myzzYBUVQQoeIegWrVqMWzYMMAdP7WdgqBr165A6YpqkEg9VdV+rVq1vCjEiBEjSn2tokszZ86kQYMGgFNUpbCq0jxffPfddwD83//9X8qtFNXjjjvOiyL5lcTLL7+8zPeXEhc1dMysUqWKl08rZVHeoKr2VxRSDhrnnHMO7733Xl7HW17atWvnncuE9j0d8+UUou9h5cqVbL/99gDceOONAAwaNCiU8cX3rGMYhmEYhmFs0BRUUZUPoJTU0vJlo8Y555wDuC5K4Coic/FAVf6q5q5bdX+66aabKj7YLFDeW6Zt8PLLL9OlSxcAJkyYALh8IuXmXHPNNYDLvZIyEGWUs/fAAw94FeBShtVxS11Y/POpU6cOQ4YMSblPfoN33nkn4FTZfCMlVfukVsfgFFSNXTmbWilL0ZIbQNxRpXuPHj0AWLZsGeCqrqOI1Cnl74NT7XXcXLJkSZnvU716dcApcBdffDHglFW5jMhvNN9oLt27dy/halAWDz/8sJe/qm0cFVTJr+5hEydO9PK7c6Fu3bqAOyYdccQRAY0wGLQPLlmyxBvjTjvtBMDZZ58NwOGHH57x9XKuKEtlLhQHHHCA97cia1JSxeTJkwEXjdR+fMIJJ8RGUW3durV3fnv//fcBFyHWvP3+p/PmzfPyrcNSUkVBLlSVmO03dI8TuhhVMVBFqUihQBCUZYfVsWNHfv75ZwA6deoEuMR6hcKnT58e4giDRa1dZ86cCSQuOnUBrgvUzz//vNTXPv744+y9994pj2k73nrrrcEPOgs6duwIuItQXaB+8cUXXkj/jjvuANyFqZAVkhZHsnGKO3vttRfgwtu6aPOfcKLIyJEjgUSq0R577AG40KoWyyo68rd/bdmypbdg0muVyqFinCuvvBLIX+hf1kxt27YF8ML9VatWLfO1H3/8MeAs84YMGeKl6UTlQlUpGwoPq+CtPCfyI4880iv2k4CgotSo0apVK68AWDZNZfHzzz97i2IVDUYFpcYkF5rmmsZ13XXXeUJBVC9Y1bSgTZs2WVlI+Qn7AlVY6N8wDMMwDMOIJAVRVKXcKKyxMaMQrT/0n28UasxkuPzpp596ocfevXsDTu2JgzIlFOqXHYpCo4MHD/bsU/zqkmyNFHZTAVyjRo1KbC+97/PPPx/G8MtE20Qh0S+++AJIhKP8NmlKhFdKh8LCKgRJRqp5UBGEfCI7LkUEsmkXGhVUtLJmzRpPCa5VqxYAzz33HODSOxSyk0rVoEEDL/SsYhiZ55dmfB8kUkq1T6k4NBu1XqqvvgPNNznlQVGQnj17ApkLPcNGqRQqphRPPvkkUD7FsHLlyp4dlbjtttvKOcJwGTBggGfNly0LFizwogBHHnkkUPjiU+HfbyFhR5gLNWrU8IrNoqaoqqlEsnr6wAMPFGo4ZWKKqmEYhmEYhhFJCqKoBmmyHWc222wzL6fQn6OqBOZ8oZW6DP/9bLXVVl5OsT+3WEqcksllTxUlpHio3aRykKQQ3nzzzZ6SKhVKCqqKIJQHl67BgUzXW7duDbjctEKjbbVo0SJPbercuTPgLNaUxyrFUQrrVVdd5eXgKi8wDgVyYujQoYCLWrz00kuFHE6FuPnmmz1bsfbt2wPOmFsFncmFnUKFHrJLy7cNkPKD33333Zxfe+mllwKlK+Dad/XdZGo+EnYNgEz6/aqizODLQ7pCzPI0EokqyTnUKkJVzYBqJnJpZhEkKvKaO3cukFAetY/lQnkaM+QDFULXrl0bSBzrx48fX8ARlY4pqoZhGIZhGEYkKag9lVa5fsN/5U8F2T41iuy5555ehbk/R1W2MflCK1kpvLImUuOB0lqo+u2bZKw+ceJEzwqo0MjuS4rHihUrAKfagKuiltIoRVX2TQ8//DCQWtWqFb8aNkiVjAoa819//eWpPrKPkXPFXXfdBbj8OlUu+x0N4kK1atUAZxOzySaJw5xMqePKLbfcArj8YymqmRg2bJj3W/Q7AuQLtUPNBrVZHDNmDOAsnkpD1kfKPc90jAo793/t2rWAq8pXswIpg6NGjSrzPZQP2bJlSyCRN69zpJS9qCEXh5133rlM1TpdYx81/RGKCug4qsdLa60dJsrrP+qoo7zIob99tPY9/1wguo1u1PhCv4tTTz2VN954I+1z5Q5TSDXfFFXDMAzDMAwjkuRVUdUqUytHXc3HyfBfuY6nnnoq4KoVk1FlqxQr+f9JfZPrQf/+/b1VqG712nxXP0oRUMs/qdnKuWzatKnXFEB5YVKqhCpUle967bXXep6dw4cPBwqXuynvV7+Kr7awyS0l5Uup/E7l+UmdFEVFRV7On15TaORlK59K+WcCfP/994BTUKWE+/MWkyuLtc9KgY4DUjaUm/rCCy8AJVs7xg011MhkHK+IhpTzQnozK5+vrPaZahbSrVs3ZsyYAZTdIlaKed26db1860zIpDzsimYpgNo2yunWcW///ff3npMpj1YRgGQnGOXNR7WuQ7mcH3zwgeeOIpSXrPOCKs2zOc9XrlwZcFGdQimqao7SvXt3b7vIR1sNQ+QnLjeO5PlJIS6UC0y2NG3aNOM1hxTVKVOmAHDhhRcCJRsAhIkpqoZhGIZhGEYkKSptdVNUVBSoxNmhQwfAtdhM+hzArUTUek2r0qAoLi5OkRhymZ9WU1phSalKHrt/Hvr/o48+ApzqtvXWWwOJfFD/cw855BDAtS/LhYrMLxeOOeYYwPlTKmcz3b6keWlFrMr58uRyVmR+3bp1A1x+kcYlpXD8+PHetvV73imPVeqwXvvBBx941ZNBVMMHuf0OPvhgILXjj/Ln/H6qQoqAVs5HHHEEr776KuDy5ipC2PunlERViSvqIYU47DahYc6vTZs2jBs3DnCRKW0b7Zf6/clvdffdd/dalEqtrAi5zE953uqGlok+ffoAufmDHn/88YBzGSkN5chm42Ma5PbTNpDCqm32/99Xnwe4KJPUZfkbFxcXe/636byNcyXM/bN27dre703zkcNKsgKezN577+3lIfujc0L7tjqolUaY8xs1apSnJCoa5yedUq79Wvt5RQhyfjpvqXYjXQ1K9+7dAXdc1ePKlz755JMDdYHxzy8ZU1QNwzAMwzCMSJI3RbVGjRpeZfkBBxzg/xzArcSaNWsGBN/NoTwrElVIv/XWW4DLL9X39tVXXwGJvCpVS/tV0tL+19/qvqI+6+UhX4qqH632r7rqKsCpl1qJJaPKQq3ocqEi86tSpQrgfB2Fumqly4vTa6RcyQXgk08+AaBfv35et5wgKNT2E1Jh1V0LnFoeREeqsOenbkf+aETjxo0B594QFmHMTzl68+fP97qoSYWSwuPvpKZj1cEHH+y5XASRn5vL/KTey6fRz+effw44r9Bff/014+dKkVPusVS25PxrobxB7a8jRowASn5H6Qhj++kY2KRJk4zPWbp0KQA77rgj4LrAFRcXe73U5d5QEYKcn5Q4denbfffdvQp3OaCURbNmzby5ZlJUtS/r2FsaYR9fBg4cCLhztR+d35W3W6tWLS96rOhjRSjU+aFdu3ZASSeinXbaieXLlwf2OaUpqnkrpjrppJNKXKD6UcgxSu3GFKZX6MZvI6UinTlz5pQwaFYoRBZUInlxoL8rcoFaaFSQpNaqt99+O5Bob6gDtOapE6dCY/rxh40SwnNpZaiQvw6SOrmqzWycWsdmQ7LtFsDy5ct57bXXCjSaiqOCQBUzxhEVglWvXp2HHnoIyHyBKq644gogcUxSA498F5IlFyemQ1ZopV2gqvGGFsClmecrBKnjqFqXFhqNK5tiS7W3FQsXLgzkAjUMtC8mm+CrcMyPQv4SfZT+dfHFF2e8QFWB9c033xzMgANA9nZKo9l3331TbvVbVTrZ7NmzOfHEEwFnYRU1+8Js0MK/kMXuFvo3DMMwDMMwIkneFNWioqKMdilKTtbKOUooHUGt0GS1oRVfsqWDQsIqvJKdk9+CSiT/r0Iemc4XqnWckLm4LEU6duyYdTs4rShPOukkr4BFLRy1rRUyypeimgsq1hg8eDDgtpO20YampGp/VWtO8fvvv3tqeRw4+uijAbe9pK4lG4zHBRmpS43666+/GDlyJFB2GPuzzz4DEvYxUslVeJSvaJVsqTIVU5Wm8MryTy1vZQmYjvnz5wPO2qksa6soItXNb7UVVbP4TAwbNgyAxx57DHBF0SoOk6KajTIn1TUqtn/gxq0URt1m4p133vGicUrhybftZBCoCF4o3J/P9rCmqBqGYRiGYRiRJG+KanFxccaVlBQPmZTfc889+RpWmcjKR0b8ajGquWhFDy5fJ511VTJKSh47dqxXGKHkdBWyyJRdSCnQOMJGRsUqRJk0aZI3xmxXUnXq1GGXXXYJZ4AhoDwi5diqeEWFGZmS6OOO8q9VKBhXVMyovMBCRyUqglpvyl7stddey1oNVfOOP//808sBzHfev3JEZYauPD4h1S3dsUR1AZmUVOX5vfXWW55iHEclVfit5L788ksg2i1/dd5Lbhuq+gPdZoNqB9RqW1FIWTrGmXfffddTVNMVFscFRT+FrCbN8N8wDMMwDMPY6MlrC9VMKA9OVbpRRCt3rX6lmur/9evXZ7Sjkir78ssvAzBkyBAg0WJVeXVaSUq1VH6k1C5V3uVLUf32228BZ/nTsmVLLzdF20k5m0KKpNqutmjRooTJs4haS87KlSt7bRaVT6TvWubl2VjcGIWhTp06nhuDLNCWLFlSyCFViGSlChL2PcrvLivnVlGf+vXrc/HFF4czwDKQRc+oUaMAZyklqym1YU5HpkiUlOIePXoACVeROKPj5fnnn59yv1oaB2mmHjQ6P/n301yRahyl6v6geOaZZ7yGDzq/K+86DiiCKDVYSr/fnSIfmKJqGIZhGIZhRJK8Gf43aNDAWwGrTajQqirsnJwgDHOVhypv1P79++u9PSVAOahSUrVazCVnTubPytfSazO1v/z/YwjMEFhtUlV5mq7FWtLn6PPLfExqgfxVpbxkQ5iGxw888IBXdasxaxWcLxW7UIbOar4wa9aslPvnz59fqlF5roQ5v+23395T6WX6rmhFvrwLg5yfIhnKBdxkk028SnopG/6qcFXnSr2sWbOmZ74exD4cxPzUVlOV7qWhCIeiOwMGDMj143Ii378/qW1SnYVcR4Ku+g9yfttttx2QW2TMfy5YtmyZN9cgoh+FbpiSDs1LkdFGjRoB5XOOCWJ+UsAVaVGkNtnBQMcRNe6Rf7j8mcPy1rYWqoZhGIZhGEbsyJuiCs4TUB0cpBpk6k4RNFFccQVJkPNTXsq4ceOARN6pquD9lKaoKq9z6NChANx3331A4VaUfqTiJ1c2hqVolEXUFNWzzjor0LzxMOdXs2ZNXn/9dcCpFuqEl0s3sooQxvzef/99wOVN+95fn5v2tVOmTOGyyy4DnApbEYKYn/Iyd9hhB8C5G3zzzTeeF6qiVar8/vvvv8s75JzI9+9vzJgxAHTt2hWAt99+G8itaj4Xgpxf5cqVAbcdBwwYUKJNqCKL6o6m44uUudWrV7Ny5cryDqEEUTy/y/1C+7mcScrjwhHE/NSS198tM13E9PHHHwegT58+QPj+4aaoGoZhGIZhGLEjr1X/uiLXqsKILqo4PfPMM4FE1b+qiaV8SB2ZO3cu4DriqLf6O++843mtKRcmKhx22GGAq2AsLi728o3j1hGmomSTLxh1fvvttxJenRsCkyZNAhId4qpUqZLymF8BUWcgeTG/+uqrOeWA5wPlC+s2We2Okn92mGTqBBe1Y2Rp/PPPP4BT6rt16+blNBoOf02JckPlJJNvlPet6Gj37t2BRERqypQpgFNdFy9eDORWRxIWeQ39F5oohgaCxOZXNptuuing2sAphHXDDTcwduxYoHC2MIXaftOmTQPw7J10TDjzzDMDLSSz/TPe2PyCQU1eOnXqBLiLdlkehmWkbtsv/yiFTgVIahM/Y8aMnN8rivMLEgv9G4ZhGIZhGLHDFNUNCJtf2ZxzzjmAUzUOPPBAIP8tJtMRlWKqESNGAG71HxS2f8Ybm1+8sfnFm41tfsmYomoYhmEYhmFEElNUNyBsfvHG5hdvbH7xxuYXb2x+8cYUVcMwDMMwDCN2lKqoGoZhGIZhGEahMEXVMAzDMAzDiCR2oWoYhmEYhmFEklI7U23oybo2v3hh84s3Nr94Y/OLNza/eLOxzS8ZU1QNwzAMwzCMSGId7W/nAAAgAElEQVQXqoZhGIZhGEYksQtVwzAMwzAMI5LYhaphGBslTZo0Yd26daxbt47i4mKKi4vZe++92XvvvQs9NMMwDOP/YxeqhmEYhmEYRiSxC1XD2ICpVq0a1apVo1WrVsyaNYtZs2bRpEkTmjRpUuihFZzWrVtTuXJlKleuzPr161m/fn2hh2QYhmH4KNWeqiJMmjQJgDZt2mR8TqVKietk/wnil19+AWDw4MG88847AMycOTOMYRohMmjQIK677joAnn32WQBOO+20Qg6pXFx//fUADBw4EIAZM2YAcMwxxxRoRNmz2267AfDiiy969/373/8G4NNPPwXgp59+yv/ACsiOO+4IQJcuXbz75s6dC8DixYsLMibDMIw4MWnSJO/67oUXXgDgggsuAOC7774L9LNMUTUMwzAMwzAiSWCKqkKJl1xyCeCU1OLizJ60UlL9z9liiy0AuPXWWz0l6K233gJgzZo1QQ05NHbYYQcAatWqBcApp5wCwEEHHQTAZ599BsCoUaP48ssv8z/AkKlZsyYAl156acZtHCeaN2+e8v/RRx9dmIGUg86dO3t/a1ucccYZAIwYMQLY+BTVYcOGAVCvXj0venPuuecCsG7duoKNy0hlu+22A+C2224DoGPHjgAUFSV8wTt37sxTTz0FxOO8IDbbbDPAHSdbtWoFwF577VXiuYcccggA8+fPB2DixIkAXqTxr7/+Cnew5aBKlSoAHHnkkQC89NJLQCINCVIjqFLeBg0aBMAjjzwCwB9//AHAP//8k4cRG7lw2GGHAbDLLrt427J169aA25fHjx8f6GeaomoYhmEYhmFEksAU1V69egFw+umnZ3yOcuJ+/PHHUt9LK68DDzyQk046CXD5gddcc02FxxoWbdu2BaBv376AW3lIAfCril27duXRRx8FoGfPnvkaZuh06tQJcIpy3PErqDfccENhBpIDm2yS+GlrpQtutTtt2rSCjKnQ1KlTB0jdnr///jvABhnZiCvad3v06AHA2WefDbjjp27HjRvnRfBmzZoFuN/mb7/9lr8BZ4n2P9VvSHEsLdqkc8dRRx0FQO/evYFEtBHg//7v/4BoKasPPvggAO3bt0+5P110bdtttwXgrrvuSrnVdrz55psB+Pvvv0MccXgceOCBgLsWuOiiiwA8Czwp5aeeeioA33zzTb6HmDPNmjUD0kcAwsIUVcMwDMMwDCOSBKaoKt9twoQJANx+++0A3HjjjQCsWrWKefPmAbB8+fJS30s5nl988YV330477RTUUANFq/8uXbrQv39/AKpXrw7A6tWrAZeL5GeLLbbg4osvBuD4448HXD7rokWLwht0yFx22WUl7lu4cGEBRlIxMuWiygUgyiiykbzq/fDDDws1nEhw3333AbDNNtt4940ZM6ZQw8mJGjVqsNVWWwEuZ1pquVQb5fkpSlOtWjVPsdJznnvuufwNOkfkAiOnkAEDBpT5mrp16wIuotegQQMAOnToEMYQc2afffYB4PDDD+fSSy8FCKShxNVXXw248+73339f4fesKDrPBWF9pwiqKJSyKvXwzjvvLPO56SKnOtYo31roOQcffDAALVu2BOChhx6q4IjD44cffgCgatWqJR5btWpVym3QmKJqGIZhGIZhRJLAFFWppeLpp58O6q0jjbw0b7zxRk+9OvTQQwEYMmQIAAcccECZ7yO/S6l1UVEEcqFRo0YA1K9f37vv559/BpyaFSfklxpHpCwZ0K5dO8DlkIs5c+Z4Sk1UkXo6ZMgQL8/tzTffBErm/Ck3ULmQa9eu5Z577kl5P0Vs5HsYJc455xygbCV1zpw5QMK9QQ4qU6dOBVwetpSqBQsWhDLWslB+7eDBgwGoXbt2ieeocl+RDlX0K5I4dOhQTjzxxLTvr+0XJccOjVU+xZn4z3/+AyScfC688EIgUUEOJesapKw+//zzAPzvf/8LbsBZoOiglPHSyFSLkg36zUaRk08+GYAtt9wSKOl7D27flV960JiiahiGYRiGYUSS0DpTVQSpCEVFRV7ekqo6o4ZWWh06dOCVV14BoGHDhkDm3NTHH38ccB2CkjnhhBMAt8KSIhllKleuDMDYsWMB2Hzzzb3HVMX57bff5n9gFSROfqlhUK9ePW9byrEjTkjhV96X/B1XrlwJwGOPPRbZauLLL78cgGuvvRZIqBnKQfW7Tih3VVXWUhWTHR+EIjdRRMc+oSidFDqpaldeeSUAf/75p/fcyZMnA3g5/1J/8o2quu+++24gVV376KOPAJdLLI9wOU+IW265BXB5i8ksXboUcN9BlPbfsqI46vomlXnp0qVejrgUcO3b/n1BftDKRc4XGlfYFEr5z4Y+ffoUegimqBqGYRiGYRjRJBKKqqr8Va2qnM7i4mIvt/H+++8vzODKQJ6UU6dOpV69eoDrqqHK60yVttdcc43XdUU5qcrRUbVgHBRVKcjKoRPvvPMOQ4cOLcSQAmFjVVSVl9WnTx+vS9z7778P4OWUffDBB4UZXBZozOkUfnAKgXI6o4S618ktRV2MBg0a5ClRfuRLrXzUr7/+GkivqEaVkSNHeoqwqovlhCIHGd0mK6lRQfm1UlKlksoF56WXXipRt6GIm6JyUoyTHW6kyCqPVfnWUfP9bdKkSUY3FOURSyH+6quvSjxHiqK+P7+iWih0vLvqqqs49thjAVixYgUA7733XspzlaO6ZMkSIHH8KcvtRu9Vlrd8IdCxR5GodLz77ruA8/MNi7xeqO68886A+5Hpwk4/5nQyu4odotZKTRdnSuKvW7eudwBVO8ayrGBWrFjhpTToIK0Dk9IDdOKKIrLheuKJJ1LuV7L1jTfeGMmTSrb4W6fGwei/PGy66aaAW3TJXkbWawBHHHEEANOnTwfcdxNFGzU1z/AvnFSEE+UiORUjaMH6+uuvA7nte7LU0YkzGVnmRQWlDZ122mneeGfOnAm4iz0tnKJkau9HgoIWCbrQUrg/GVmG6bfkN05PTheQ7dR+++0X8IiDQYLKkCFDvAsbP0p1SHeBGnV0TJw9e7YnXMiU33+hKtTIQQurdKh181lnnQVE6zgqyzdZcmVKf5g3b563GA67wYaF/g3DMAzDMIxIkjdFddCgQZxxxhmAM3/ffffdAdh3330zvq5NmzaAUxijIpHL3F+q8JNPPumt/HMZ4/jx4wE477zzALd6UWK62smuXbu24oMOGBWrNG7cOOV+2a6owCGu+EP/foV1Q6F79+6AUwLE3Xff7akgstv517/+BbjvJkpKgFQshWH9KGwqNSOKSE1TVCKdFUxZqO10OpucqDU46Nq1K5CwtFN0SiqTiLKSKmQXpZSY0kLzirTp+FmanZGKart16wbAAw88UOGxBsnw4cMBp+InoyLGxx57LK9jCoM1a9bw0ksvZfVcpTikK4YTstnKpMoWkkMOOQRwVnaZGDp0aN5aFZuiahiGYRiGYUSSvCmqxx57rGeNottsDHLvuOMOwNmOyHpFNh35Rgn+p556KuAKp4YOHVoutXfNmjUAzJ8/H3CKqixnpChHqYGCxjZu3LiU+5VT5ldENhSUOxcH0imdimAogV//33TTTSnPUwHcdddd59nfyKRbFmzDhg0D3O8wW7UhLHbddVev4NJvk6OohVb/yhEEt8/6LYKigqJNPXv29IrDdMzJhI6vycdV5QlGBRW49e7d27svyhY92ZJNkZOK3vQdKEdVuY/KGa9du7bXrlJtZWVppecWCuWm7r///iUeU16urNVyiQqka78dF3Q8TXf+0zFXj+k7imKkNIqYomoYhmEYhmFEkrwpqk899RRvvPFGyn2ZFFXZdpx//vme4f8ee+wBwGuvvQa4dm35MiLXKldtUTXGjz/+GMh/a7dCoO0lex9/bvEjjzwCRM86ZWNE1apyXahevbqXc6TfkNQa/caknk6aNCnlf3CWQS+//DLg9gFZJunz8u3OISeRadOmZTQcl/2dohLJrVRVVS+j9kLbbilyJKsiqW7Dhw/n6quvBqBTp04AHHjggSmvPf/880u8n46PI0eODGfA5UT50FKhwKlOGzrK3ZTDg84t69atA5wTwocffugpl9qHZWVVKEVVjRSk7PodC8A52OQyxquuugqAFi1apH1cubBRRI17ZCmnPP5klIvqbzUfJbbeemsgcxtUnSeWL18O5Nc60xRVwzAMwzAMI5LkTVFVrmk2aEV54403epXjqixXOz2pCmEbzQp9npoRyP806JyadN6HUUFV71r9Cq2wpAZtqETZf9OPKqWl3lxwwQWe4qbcMSkANWrUSPk/l1W/VD2ttvOtqGr1L2U1Hccdd1zGx+QPO3fuXMDNRybl+WbKlCkAnrm4KviT8960HeV3qJxU/a9jyNKlS725r1q1KuyhVxg1fMkFef2Wtv2jinKmM1VOz58/n5NPPhkon/tDGGgf86v54Kr7c3ECkWuOjPV17hc6Fv3000+5DzZk6tevD7jowC677AKkRogVgbr33nvzPLrcOfvss4HM+5rO89pWOmbmA1NUDcMwDMMwjEgSiRaqfqTKrFixgocffhiAW2+9NeU58krMl6KqqlmtllStH1ROmzpd6f3ffvttIDqV5lWrVvW8Y4W6plxxxRWFGFLekXdonJTVdJ1xlBco5wqxePFiwOVwLlu2LJI+f+A8UUvzYPYj5UqqjZRkcPtyVJRH/e51q5zg0pADg3J1V6xYUfDq8Gx57LHHvGNqLsglJU7tYstC3qmHHXaYp26V5oyTD/SbUTTGP56PPvooxcEhG7bZZhvvvK68Tv/5tWPHjkC0XDk0JkWrhKJK2marVq3yIiJR57LLLiuz+52OL/I7ziemqBqGYRiGYRiRJJKKapRQP3utloRWvRVFFZ/yZ9WKUl28VG1daC655JISuX5vvvkmkLlKMK74O1LFGeWN9ezZ08uhko+jH3W9UfVxcnW8SFYhweWeJzsE5APl7uWS033LLbcALpc6eS7Kt1q5cmVQQzRKwd8//JdffilXHubAgQNT/pdnblQiUeVBnsWqsAdYvXo1ULjOjHLdUUW/n3vuuSfrc5Xyii+66KISnqPyCFa3vKg4yFSvXt3bZ9P5FINTUp966imAMhXKKNG4cWPPYSQThfKuB7tQLZMmTZoAzo5KBGVO3blz55T/9WPPV2uysthiiy0AZ98D7mBy8803F2RMYbMhXahqf1q2bJl3oXrooYemfa4KiGSDJBsrcPup9gO1IZXtUb5Ckzp5+9u9ZoO/sQHAq6++CiQu5I38ccwxx6T8f9ttt+X8HhMmTPAW+J9//jngLnCiUnyUC7JmSjbRl72cWmyrPXW+UQGNH4Xk/daT6VD6gFLI0qXt6SLvySefLNc4g6ZKlSpAIuWhX79+aZ+jNIXp06cDJY+RUUZpYLr4Lo1MranzgYX+DcMwDMMwjEgSeUVVlkj+EN/s2bPz8vkqnFGRhUxxg0JmwWLUqFFAdFZjsqLaddddvfsmTJgAuND/xkKciqj8DB482FNSFfJW8ZT2QSmqyfuelBzZq0gVUZFjvkNzSsHx29jkgtoWTp8+3Uux2RBaGeoYqVt/ulKUyUUBlYp64okneqkaKgyMo5K69957A67JgxpxgDvvPPfcc/kfGO53JlsqPxpzNkWXAwYMAFw72GSUwjF69OhyjTMstK9lUlPBNTs5/fTT8zKmIGnatGnKbVSJz5HMMAzDMAzD2Kgol6LavXt3b1V01113AfDEE08AzhS2PGy11VYAnHbaaUBi5SWrFeXAKacs3ysvv1rRrFmzcr/XtttuC8CDDz7orWReeOEFwLWkLDSbbbYZkDCK9xOV/Fkje6ZPn07fvn0B95tVXpL+l+H/fvvtByRM1JWbpv1e++mdd96Zp5GnIgVNhQpjxowBoFq1amW+VscOqcF+e5m4o2OkbqOsLsoOLBdatWoFuALBGjVqMHToUKDwrW9zQcd//ZYOOuigtM+bOXNmVrZkYaLoi87JftSCOBlFQfXaww8/HEhvbSUlVpZH+WzLmQ7lpCpn2l/slYxaMiunf0NFdlyFxBRVwzAMwzAMI5KUS1HdfPPNvfZhsnzp1q0bkJs9iKqr27Rp470vuJydZGSA36tXLwA+/PDD8gw9ZzTGWrVqAW41qDEqhzObqtUddtgBcKvHPfbYg0mTJgHRy29Rm8bknNxff/0ViF4eUdBcf/31QEnrmzga/iejPFNV6g4fPhxwisdLL72U8bWyrtLvfcmSJaGNMxvUblNWWqeeeqqnfsyfPx9wOdTKqVZlcr7bvBolefDBBwFnd3TFFVd46qF/++j3qHbVOhbPnTvXi+TlkypVqtCoUSPAuQ2UFWWqWbOmZ6l2//33A05x9DtmfPrpp0CiKj4od5nyIocXVeH7FUYdD6pXr+4541xzzTVAaq5tOqZNm+a17Sx0TYZsKKX6XnLJJRmfq+O/qvsLrQKHzbJlywo9BFNUDcMwDMMwjGhSLkV1ypQpnuG9FEXlu8mrUDltpfkrZnqOTI3/+OMPL/9jypQpAOVqs1cRpMJoxZxswgwwZMgQIDH/r7/+GoCXX34ZcH6Vqpg/99xzAaccP/LII14lZFTQ2KReJCPf1E8++SSvY8o3UnD8xFVJFTLll6H4f//7X8Btaxn8y9z7zTff9HJBld8Ztep4qaUTJkzw1JmNFXlsKq8/TlxxxRWeEbzmocYMOrfI01nG423bti2IEnfCCSd4kTCN9aeffkp5jv/cttVWW3HAAQeU+r7PPPMM4KKTUagFkLqdqRGGahn8Lc5LQ36jHTp08KJ0hUI+6drXMh1DfvnlF4YNGwa4ttQbupL64osvAqaoGoZhGIZhGEZGispQPMtsN9OhQwfAVb6pYti/orzjjju8FaIcA9I9B1yLx6Dz4IqLi1PMWLOZn1AerW41b60os1GOVSGpldnkyZOz/fisqMj8hKoeVQmuTi9Tp071cnIKtcIKYn7ZoFxUdWaSqphJaQ2KfM2vUNj8wkW/1fvuuw9IRIOC9EcMcn7Kzxw7dixQenW10HFH1f+LFi0q78enJdv5ValShd69ewOw1157Aa6lb+3atfVavWfGz1N0omvXroBTsMJSUiuy/dSVSNGY8nDmmWcCLuKo/NegKM/8dE7T+S4TEydOzGofDZMwji9yoBgzZgytW7cGnEd9p06dALxIcdj455eMKaqGYRiGYRhGJKmwohonglyRtGvXDnDek+mcCoQUYvk3vvXWW+X92FIptKITNja/eGPzyw/KK3z//fc56qijgGBUujDmp8rwK6+8khNPPBHAy+VUl74VK1YAzlFm9erVFf3YtFRkfvIAV86jOjYln1+V2zho0CDA5bz/8MMP5R1yTlRkfupyJrcf1VZIDQb4+OOPAbz8XSF3kbBzOsszP5235XhSr149wPn8Kp+2Z8+eBc9JjcrxJSxKU1TtQnUDwuYXb2x+8SYq85N111lnncWRRx4JwLx58yr8vlGZX1jY/OJNEBeqKhJv0aIFEK024Rvb9kvGQv+GYRiGYRhGJCmXPZVhGIYRTVSU06xZM7755psCj8YwoosaB+24444FHolRGqaoGoZhGIZhGJHEclQ3IGx+8cbmF29sfvHG5hdvbH7xxnJUDcMwDMMwjNhRqqJqGIZhGIZhGIXCFFXDMAzDMAwjkpRa9b+h50DY/OKFzS/e2Pzijc0v3tj84s3GNr9kTFE1DMMwDMMwIoldqBqGYRiGYRiRxC5UDcMwDMMwjEhiF6oFoFKlSlSqVImrr76af/75h3/++Yc+ffrQp0+fQg/NMAzDMAwjMtiFqmEYhmEYhhFJSq36N4KlcuXKAFxxxRUA3HzzzaxduxaAGTNmFGpYhmEYhmEYkST2F6r169cH4NFHHwWgWbNmVK1aFYC///67YONKplKlhHCtC9ShQ4cCsHbtWp555hkA3nrrrcIMrgLUrFkTgOeffx6Ao48+GoD169cD0L9/f2+uUUGLhRo1agDQtWtXALbeemsOOeQQABYuXAjAX3/9Bbj5+Hn66af54IMPAPjnn3/CG3RA1K5dG4Cff/4ZSGwfSCyYjOij41q1atUAGDhwIABXXnklABdddBHgjoXJ/PHHH0A89lPDMPKDztlHH320dzyRaDZz5syU515//fV5HFkqFvo3DMMwDMMwIkmpLVQLZSgr1au01X+VKlUAmDJlCgDHHnssAH379uX2229P+5p8G+Zuv/32AJxzzjmAU64U7u/bty933313YJ+X7/lttdVWAMyePRuAPfbYQ+MA4I033uCoo44K7POCmN+RRx4JlEy1WLNmjaegrlmzBnCKsdTXdEgR7927NwDLly/PdUgeYW8/Kao//vijPg+AU089FYDJkycH+XElCHt++++/PwDXXnstAKeffro+B0go4AA33XQTAO+9916QHx/q/GrUqOHtY+VRNlq2bAnAokWLAPj++++B3BTWjc1w3OYXLM2bNwfgwAMP9O677LLLANhpp50AeO655wB3Tvn0008Bd54vjULPL2yCmJ8U1Ndeey3nz9c584Ybbkj5PyjM8N8wDMMwDMOIHZFQVA866CDAqVJS5p5++mmGDRsGuDzB6tWrA/Dss88C0KJFC8Cplk888UTGz8n3imvUqFEA9OjRQ58PuNWMVI6gKNSK8rbbbgOgV69eGgcAH3zwAccddxzgVLyKEMT8Dj74YAAeeeQRAP7zn/8AMGfOHC93c/HixQA0btwYgH333ReAOnXqANCzZ08AdthhB+99ld8jta48hL39pCzqN3LGGWcAMG7cOAC6d+8e5MeVIMz5VapUycvNbN++fcpjmrf2yxUrVgBw6KGH8u233wY1hFDn16VLF8aMGRPU21GvXj3AKavZkK/jy2abbQbAY489BsAWW2wBwLvvvpvyvCFDhrBu3ToAVq1aVeHPDWJ+e++9N4CXu/76668DidqJQucHh739dt99d8Cdi88999yUx7UdFakq49oDcLnV77zzDgAdO3bMGLWKiqKq/bd79+5eVGfnnXcGYMCAAQCMHz8+5/etyPyyUVKllPrRuc2PtlFQmKJqGIZhGIZhxI6CVv0fccQRALz00kuAyzsVQ4YM4c033wRczsrEiRMBPKVOlculKan5Rsb9foVq/vz5AHTq1CnvYyoE++yzD8cccwzg8gMLzYIFCwDYc889y3zu22+/nXIrpMI++uijnHDCCYDb1g888ABAoEpdUEjBUN6XkHIcR5Qn3bNnzxJKqli6dCkAO+64I+CcQs455xxuvfXWPIwyeihKIIUlk7NFPlH+uLaJXDik3Ohx0aJFC2/cisZNnz49L2PNhBwY9Fs7/PDDvds5c+YUbFxhs/POOzN16lTA/c7K4scff2TatGkAJWo1Hn/88ZT3atq0KQBNmjSJzLnEj84FuiZJ3l+1D19wwQVA+RTViiBF1Y9U1Gzy3v3K6muvvead38PGFFXDMAzDMAwjkuRVUVU1f5MmTQAYPnw44HICdXUvlWTcuHFeTuHZZ58NQKtWrQDo168fQKQUEeWh9O3bF3D+qcqvadu2LQA//PBD/gdnBMYvv/wCwOeff+7d98UXXwDw66+/FmRMufDf//4XcCt/5StKaVQOZxyQMn7dddd5940ePRpwKoHcDubOnQvANttsAyRU2AkTJgDw5Zdf5mO4keHll18GoFatWoDLBSwU3bt391xRtL2EzhMPPvgg4PJAH3zwQTbZJHEK69y5M1B4RVV57n769evHd999V+prpbqtXr0agDvuuMN7zW+//RbgKCuOvHyvuuoqIBElVOW+P/f0s88+A5wbh1i+fDnz5s1L+/533XUXEK3zux8dL5WLq+sXXeekQznL+cavmObiHOJ/rpTVo48+2lNqw25YZIqqYRiGYRiGEUnyqqgqh0zVuVKfdFUuX0OtVFatWuVVzOu+lStXAi5PMCpcc801XjW4qhu1emrXrh2QebUdd6QESEGOQr5bGKiaUwrPaaed5j0m795CK1PlQYrqoYceCjhHjTigSmNwuaiKtkiZkuPETz/9BDhFtUaNGp4ityGyYMGCMqM3ha5E32+//YBEtz6pu4pYqIbBn1Otivp58+Z5al1UkPqk3FRFDxUJLA2/O8Ull1ziKY433ngjgJcHWijkVXz11VcDcNZZZ5V4js7jclZ54YUXAFiyZEnWn6PoYxTRcVL1CFL407kYSAlXtCCTx3u+CLq7lCmqhmEYhmEYxkZNXuUEKRlC3ncfffRRyv3KkZszZw4nnXRSymNbb701APfffz/gVK1CqXiqerv++utLqDNSBD788EPA5cjp/jFjxhS0f25QaCWpbaD/33777aw6ikQdVW/ec889AOy1117eY6qalsoTB+SkITVq1113BaB169ZAvBRVOTL8+eefXr6plFQ/hx12GADvv/8+kKgo3nbbbYHc1J648Morr3DHHXcALhIVNdRRsFatWnz11VeAUyHLyun057JGASlo8vpU1PDqq6/25pcJqVM6P1SvXt37LpRPXmhFVRGMDh06lHhM52J1l8oFeas+//zzgPsuohSdk+IvpVjHzUysXbuWZs2aAbBw4cJwB7eBk9cL1d9//z3l/y233BKABg0aAK44Rfen20m1I8vqo1DhZpm96+KltBDidtttB7hWlaJ///6eIbDMrdXiMA4XC5pXuvAPwLp167x2pHFChtWyQGvTpg3gUjoUQh41apTXkCJO8/zzzz8BvJaxcUYngOHDh3PKKacA7mSuY4Tmqe2n3+6KFStiUzi2ePFiL91J4fLkVpTp6Nevn3d8VMhR+26h2WeffYDUUOTMmTOBsi9QRVRtipLR/plNswb/RWjDhg1LpD0UGokQ6cLc5blAVeH0Qw89BOC13Nb5fNmyZQA8+eSTQHatVMNC+1vDhg1T7s9kfF+9enWvqUqcL1S1aPDbU82YMSNvQpuF/g3DMDpkidQAAB9TSURBVAzDMIxIkldFVasmhU5l0qyQnFZRsk7ZbbfdvJCVTPRViFUoNUjWE2PHjgUSY/Qj5VjFN5lU38qVK9OoUSMABg8enPLYvffeCySKtCB69iTgUjnq1q1b4JFUnHPPPdezOPrXv/4FuO0m9UBFHlLGlcqxoaD5+os64sBvv/3m/ZZkv+VvDaviB3HnnXd6BVhRZ86cOZ5CrNbLOmaoBXU6ZJX31FNPAdFRVBW21zHyr7/+8o6pmdCx9/zzzwdcJC6KqJB20KBBQKL965133glkXwj81VdfefuyirNUzKSCpSghe8ayrN5UvNm2bVsuuugiwBUk+ZGSqvNgIclkvyX89//999+RK/bLRLIymqllqh9FQPKBKaqGYRiGYRhGJMmroioVVCtL5TaOHDkScCsS5ZI99dRT3koqKobcWtkqf1FjnjNnjpfDIiVHuY41atRIea1uN998c+87UW6qEukvvPBCwNmSRFFRHTBgQKGHUG60guzVqxeQ2EaZco2UL3zeeecB8bSgygaZpssSLk45rHfddZf3W7zpppsA6Nq1K4CntKolp1BL47jxyiuvAE75lmpXmrKq70L7faGt8qSkiUy/PXAKnGzH/K+F6CjFQsdxKcazZs3y6hCypW7dut65QgWCmQoF88Ubb7wBuN9O8m9KObYqylTNifJQVYAlRVxtfMHZpH399deAs7+KUjGu2uMOHToUcMVVut18881Tnj969GivSUVUUTFwpharpdG8eXOzpzIMwzAMwzA2bgridv3tt98CLudP+SmyiOnWrRsQrdVU1apVAbeqFzLyHTFiRIlV/X333Zfy/4gRI1L+b9y4sfe+WtnEASkcsirJZPivVXGU0fdfVFRUQtXRfGS7oraByhsbO3ZsrNXVjz/+GHBtSOPMmjVrvHaLyt9TzrEqiaW4Klc17jnG06ZNA9y85ALw73//G3CV9ZAwjwfnTqLc1UIrdGKTTTbxlHA5FOh3JmcVv1UhOGV41KhR+Rhm1ija9M033wCJdqhr167N6T2uvPJKT5F99913gcLbqMl2S8dEmfnvt99+Xr2G7nvxxRcBt930eHIOvCKlOo/688ijhOa1YMECwJ3ndL8iGrquUaQ4ipSn/kCqqVTUdC1UZdcZNKaoGoZhGIZhGJGkqLQr66KiokDLfqtUqQK4FbLypoRyrbKtOsuV4uLiFMksl/nJ9N2fi6EVhSpyy4uqcpPbcoJrHZuNt2BF5pcLMqH2z9lfLd60aVPPXD4IwpjfwQcfDCScKJQbrVwkqa0yo1ausfj666899Wf06NFAxfI687X9hFr++pX+6tWrA8HnqOZ7fo0bNwbgf//7nz4fwPNblSdzUOR7fpnIdCxJRlGR77//Puv3DXJ+xx9/POBUt3RIvZPvbbpz1cUXXwy4BjAVISrbT7mOM2bM8I7/LVq0AFzzmPIQxvzk3vDKK694x9KyPM2lDt95552eE1AQ5Hv7KWKjOhLtn9oX0+VSV4Qg55eNoqprnRtuuCHlf3H99dfTvHlzoGQr1fIoq/75JWOKqmEYhmEYhhFJ8pajWqVKFc8btEuXLvn62AqjNmnK6fKj1X5F0epMyq1aO8YRuR5UZPWfL5RvpFuAu+++O+U58vmV0io1qF69ep6iqi5dqqpet25deIMOmXbt2gHx6PyTjl122QVwOaj+bjpSyCtVqhSpFo3lpVOnToBTMUqr/o8KamOrtqI77rhjiefo2JrJh3r16tV59XIMC3/kRj7iBx10kNfR6Isvvkh5blSOLxrHN998U6KFth/ljssxRy3U44YUbznzKJKo850iw1FGKmmyIlqWgurn+uuvT8lXTb7VeTCozlWmqBqGYRiGYRiRJHRFVVWm9957r6ekPvLII4Dzo7zggguAaPbDVR5fq1at0j4uT9jmzZuX6HWsrhTyiPN3qahVqxZNmzYF4LLLLgOckqrcrUL7Habj3HPPLfVxrciiUlFcUZRn2759e8Dl6Pbu3ZsTTjgBcJ3ThPKso+xFqgpbKQBSCuKuqOq36s8pFsqLmzp1Kj/++GPexhUU6oym6v799tsPcNGfOKBqeHlN9+3bt8QxVt2dtL0UkROLFi3ik08+CXuogaGuUv7aDOV2yjM1OQIgr1HNUwq0IkDTp08HKHHuyRdSzOSdWhp16tQB4qukiuHDhwMuz1vbSy4/8oKNMkEpnX4VNqz6otAvVHUB1qVLF6/96bBhwwBnPyLJPI5hHP34zj//fM/IWKhQQReq/sKFTTfd1LN4EiogUCpAVEI84A60bdq0KfBIykZhQx3Yg2TevHlA4sJVFws6ieqCVeEgbUftA1FCrX4nTJgAuJQcbee4kmzLBK5ZiI4z2ZxU803Dhg0Bt/AtDaU26DVxRgWZr7/+uteYQXz66acA3mLQzxlnnBHu4AJG9kyXXnop4C7YZBjv5/vvv2fx4sVAZtumQtkaHnrooYBbvINrSiOTe7UuVrqCWpCqLWq/fv28pgBxQPPwNw7RNlIR48aIUgjCwkL/hmEYhmEYRiQJTVG9/PLLAbjllluAhHqqkIdWlApza7W4cuXKsIZTbqSeKYyogoWdd965zNdqflLX6tatm/G5UlLbtm0LRDMNQo0YFPLwo6KHQlO1alVvVf/rr78CeGbwQZu8P/744wBsv/32KZ+jZgeDBw8GnHpphM/pp5+e8r/sYlTsEQVFVW0l1Z5Xxu4K44eFIgAyJY8K69ev54MPPkj72LHHHpv2/rilbSRHYsCZ96slp4o0FeZv1apVZFqHC0VbJk6cCKSe05QypMio2q3qWKh9W0r4+vXrvf0xDqhxgWzvhJpN5GL1tqFQkRasuRCNKwvDMAzDMAzD8BG4oio1Q8rSrFmzgITJtlSD3r17Ay5nzG8HFCWkmOlWBsfKD+vYsaP3v4oBlOiuJHmh4rGlS5cCCZN5FeqcfPLJQDSLp4TU3kz2I1Gx+lm3bp2Xd3nXXXcBzhJFxQfKl37llVeAiitMLVu2TPlfakkUc1M3VKTobL311in3K6qjlrGKcOy2224FUeVq1KjhKRH+vMyw6NChAwCTJ08GopX7XhaKVsQd5cvrVmb+avH7559/AnDiiScC7jwRJVRTkS6q5q8xeeKJJwD3e7zjjjsAF3lr3769Z1klVTnK9OjRI+39cVP2k/Eb9WfzXN2WVjiVzfvlgimqhmEYhmEYRiQJTFFVlbVWUUL5G9tuu61nmN6gQQPA2TzIqiQOSHnzG8VvsskmXl6u8kvVolG5OtOmTQNcDlKDBg0YO3YsAGvWrMnH8ENFthxRUGvGjBkDuEpNVSVKFZaCrdzg+fPnl2jn+M477wCuqvOss84CnLl/cuMKf+MHKQRSSaKM30KrcuXK3m2cFGFtJ+UDK6882eAfXERAz883lStXzouS2q1bN2bPng3AihUrAFi7dm3onxs0yunMpu1jnJg0aRLgWhbrvBFFJVUsW7YMcPUkyrUGOPPMM1Oeq+eoZbrcOOQGUFxczHnnnQe4CJeOx1GjV69eJaJmqnfQ2OOE7Kmkis6YMaOEIu5vj1oaFWmdmg2mqBqGYRiGYRiRpKi0VWpRUVHWS1hV7/lXF1rBV65cmSpVqgBu5ai8wXxV+xcXFxcl/5/L/OJA2PPTSl+5VUIV9vKA/N///hfkx3pUZH7a97bcckvAmaNLBWjUqJG3Pwopc8ot3mabbcr8HFW46rvIRZEs1P4pVUT5i2oZO3r0aHr16gXA33//XeHPydf8VH2reSknUKq38sKPPvroQNX/bOdXs2bNQHPR5dWr1sVi1apVgSqohdo/9RvSuUr74pZbbun9NoMgX/M75ZRTAOe7qXx2qVHfffddGB8b6PyU86zmPf///fQ5gPNV/eijjwDXJtdvlJ/8fhVpMhLk/Pbcc08ALrroIiDR5EZ+t1KVpbBq+4VNEPPzK6kVYcaMGYEqqP75JWOKqmEYhmEYhhFJQvNRlfqmHE5wVYHqTBFF31QjM2rTd/HFFwMuX1d5yGEpqUGgPEypFbpVm8YqVapwxBFHANCiRQvA5eaotaGQ9+WiRYu8zipSR5RrHBUHhGxQ1aryduWUcPzxx3tKdBCKar6Qq4iUfik5moNaxkYhl1rot6MKcHHaaacBiejTJZdckva12rej3K43SLRdg1RT80n//v0B115cKldYSmoYyC1lypQpADRr1qxEhy3lhitCkw5FP6LiM636A53r1E2suLjYO04OGDAAyJ+SGiTKJa2Ioqp2qUG1Yc0GU1QNwzAMwzCMSBJYjmocsBzVeGPzizf5mt+mm24KOJcG5W6OHz8ewOuQFzS2/cJBOao//PAD4Ly6g+4yF/b85L2tKI5UuwsuuECfH+THlSDM+TVv3pwDDjgAcJ2bzj333LTPVS7ru+++S5s2bYBgnH+CmJ86bimPWGP966+/aNq0KZAaJc4nG9vxJRm7UN2AsPnFG5tfvLH5hYMuVNWsQ61Ggybs+alwU6lvO+20E+CK/cLG9s+y0b6ltu5qYXvdddeVsG/KNxvb9kvGQv+GYRiGYRhGJDFFdQPC5hdvbH7xxuYXb2x+8cbmF29MUTUMwzAMwzBih12oGoZhGIZhGJHELlQNwzAMwzCMSFJqjqphGIZhGIZhFApTVA3DMAzDMIxIYheqhmEYhmEYRiTZpLQHN3T7A5tfvLD5xRubX7zZ2OZnGEY0MEXVMAzDMAzDiCR2oWoYhmEYhmFEErtQNQzDMAzDMCJJqTmqhmEYhhEl2rVrB8CkSZO8+3bbbTcAlixZUpAxGYYRHqaoGoZhGIZhGJHEFFXDMAwj8myySeJ01b9/fwDWr1/P33//DYA1rjGMDRe7UDUMY6Olfv36ALzzzjsAXH311QA89NBDBRuTkZ4hQ4YAcPDBBwOwbt06Hn74YQA+++yzgo3LMIxwsdC/YRiGYRiGEUlMUQ2BbbbZBoB69eoBsNdeewGuCKBDhw5AIlw1atQoAG666SYAvv3227yONVtOOeUUTjrpJAAOO+wwAL788ksAvvvuOwAWLVoEODXqhx9+yPMog2HKlCkAnlrzxBNPFHI4Rohce+21AGy99dYAfPTRR4UcTrlo2LAhAD179gTg0ksvBaCoqMgLjZ9//vkAPP7440BCjYwLmt+///3vlPuXLFnibT/DMDZcTFE1DMMwDMMwIklRaUnoYbXI23PPPQG46qqrAOjatas+D4AJEyYA8Pvvv5d47ccffwzAiy++CMAHH3yQ9eeG0QJQOW6tW7emc+fOAGy//fYA7Lzzzlm/z1NPPQWUVA1yIYz5Sc145ZVX2GmnnQD466+/AKhcuTIAlSqlrneeffZZAE4//fSKfnwK+Wrh+PXXXwNOUe3bt2/G59apUweAn3/+ucKfu7G1qCz0/Jo3b+4daxQV2Hfffcv9fvman3535557LgBDhw4FnCosvv/+e7bddtuU+2TjVJ6cznxvv2rVqgFw6623AnDJJZcA8McffwDQo0cPHn300cA+z1qoGkY0MUXVMAzDMAzDiCR5zVGtWbMmALNnzwZgyy23BEpaiyQrcatXrwZg0003TXnOmjVrgNwU1SDYfPPNAZg4cSIAzZo1A6Bq1aolnvv9998D8NZbbwFODVZlcTLVq1cPfrABoNy2nXbaiTfffBOALl26ALDDDjsA0LZtW8ApPcpVjRObbropgwcPBmCrrbYCXK5qJjbbbDNP2Vde4Ntvvx3iKPODVP0jjjjCm5dQ/vF5552X93EFxWabbQZAr169PBWyIpGMfKExHnTQQQD07t075XEZ4Cvv/bPPPvPyqw899FAAxowZA8Cxxx4b/oArSIsWLQCnpIoZM2YABKqmGoYRXUxRNQzDMAzDMCJJ3hTVoqIievXqBZRUUqWKvv766wC8++67ALz//vv88ssvgFMyxYIFC8IfdBr22WcfwKlukydPBmDt2rWMHz8+5blSUJXzePLJJwOpiurcuXMBGDBgQHiDLgfHHHMM4NSMX3/91ftbiqluX3nllQKMMFhGjx7tqYSaj/bDTNSsWdNTqqQ8x0lR1XybNGkCOKVcxupFRUWsX78+5TVSz/fff38A3nvvvbyMNUj2228/ANq0acPzzz8PwLx58wo5pDK59NJLueOOOwCXy//jjz8CcOKJJwLOCzY5QiVV8tdffwUSebngtnkU563omT/ypJoFU1INY+PCFFXDMAzDMAwjkoSuqB5//PFAwjvUn9emlXGc8t2kQBx11FGAy5VNx+677w64in75kIrFixd7jgdLliwJfKwV4cwzzwRcPt+yZcs8xWZDQvnQ5513Hj/99BMA/fr1A+C3334r8/VSt3bZZZeQRhgc+p1dd911gHOlkGuD8q6XL18OwMKFC3nggQdS3qNWrVqAiyQoTzlOjBw50vt7xIgRQHqHkSigSNKll17q7WsaqyI0pan48ktVdEeezlFESurtt98OuGOseOaZZwDzNTaMjQ1TVA3DMAzDMIxIEpqiqu5L8qJcv349w4cPB5xys3DhwrA+PnRKU1KVy6kcK7/qpOr5li1bRk7JkTKo7ScaNGjg5dqWBylz6tdd2veXD6TeKDe4uLjYU1KzVY5r167t5QOW5kdcSORZPHnyZM8HV+4M+h3KlUI5kPLJbdy4cYn3U06nIgFxQr6jhxxyCJDwYp45c2Yhh1QmckpRdAacIqzjSGlIUX3//feBaCuqclC58MIL0z4+duzYfA7HMIyIENqF6qmnngq4sGL79u09I/gNkerVq3vG2z169ABcUYr4/PPPAffd/PPPP3kcYXaoeO3PP/9Mub9SpUpeIwOFxKdOnQo403sVbqRDrQ5lrH733XcHOOqy0YXpHnvsAeAVviUbvCskrufccMMNQOYUAIVeo4guUF9++WUgsdB44YUXALd/rly5EnAXpn6Si1lkfXTOOecAznQ9DqilsS7wtKgYNGhQwcaULSraBBfy/89//lOo4YRGjRo10tr2AaxatQpwv0N9JypsBbfYKvQC2DCM4LHQv2EYhmEYhhFJ8mZPFUX1MEguuOCCEubofhRWVwh94cKFvPTSSwDcf//9AHz55ZfhDTILVFDUvn17wNnaLF261HuOtqWaMSiU7LcQ23fffdliiy0AlwLSs2dPwLVZXbFiRfCTSEJKqj5PxX3pQvVK0ZCNmpTvadOmAXDRRRelPD/ZNP3VV18NctgV5vLLLwcSSiokFFEpqT/88EPa16jdprbVEUcc4T2mQpY4KalCynjt2rUBuOuuu4BoWjP5OeOMM7y/VZSpyMyGRIMGDTxLPD9S81Xk98gjjwDutwwwbtw4wB1fTFk1jA0HU1QNwzAMwzCMSBKaoqrClFatWgEwYcIE+vbtC8Cdd94JUMJMPI6oAODWW2/N+Jy///4bcCqe8nYbN27sFazIQP2KK64A4NNPPw1nwFkyZ86crJ8rhVW5ZOneQwVKUiGV63jLLbdUaJxlIUVX6ossflSI0rp1awB+/vln7zVqUak8WhV3yOz+uOOOA6BevXre+0XNuitZiYNEEVsmJVXNCpSjG0fLqXQ0atQIcDm5aml83333FWxM2aI8zG7dunn3lafJSbVq1QBo2rRpMAMLic6dO5e4T41gZEeVTkkVKu5ThOb6668PYZSGYRQCU1QNwzAMwzCMSBKaoqo8KiloTzzxhGfkfMABBwDw9NNPA65lpb/SPA6ohWZyhb+UU81XaquqrHfddVcgUTWulf8JJ5wAOBUvnWoQZ26++WbA7Q9SQMJWVFXRrlaf3377LeAU3WQlVchAXS4GylmV6iM1tnr16t62/u9//xvG8APjwAMP9PJVhZRiGavXqFGjxOtefPFFAKZMmRLyCINHzRzEsmXLAPjoo48KMZyckPNE/fr1vfuUP54Lyh/X++gYG5UcTuWlXnDBBSUeU06/cnNl1VUa9erVC3B0hmFEAVNUDcMwDMMwjEhSVJpReVFRUWAu5jvssIOXa9SkSZOUx6SsnnXWWUF9XFqKi4uLkv8PYn7KgRw4cKCntH322WeAq4pftGhRxtdLsZKiOn36dKB8imoY8wuKKlWqAK46Xp6su+22W9bvUej5yXRdDg3JLR6lXFWEIOf3+uuvA3DYYYdlfI4UfilWUp01v99++83zis0lZzkT+dp+8k1V3rDURLkYhFXtH+T8NNbk713tjHNRQ6WS61ikvM90zRzKIoztN2bMGCC4BhJqLJJOoS0L//wMw4gGpqgahmEYhmEYkSRvPqpfffWVV3mqqv8uXboAcOaZZwKuGl4dSgrtKZoNqnTv3bt3gUcSbZQrqs5XUlTjhJwY5FEatUr/ZJT7K1/JZD7++GMA7rnnHgC++eYboGTHoy+++CIQJTXfjB49GnBKqjrGxcE3NWjOO++8lP/1XWyoqFWsYRgbDqaoGoZhGIZhGJEkb4pqMlJ5Jk+eDDj1R1W6qvaUn6WRf4488kjAVckvWbKkkMOJFJ06dQISnqzq4hQ1nn/++ZTb0lDldZs2bVLuV5V8XGjevDngjiMzZ84E4uGb6mf+/PmAy3eXU0gubL/99gwYMCDlPuXAFxp1QQuqLkGd0xQlMAxjw6EgF6pCtlS//fYbAH369AGgZcuWQKJpgGyNjPywzz77AHitXa+55hqg4heqderUAdziI5sLqKiiC6Hi4uJIh/+zxW9b9fvvvwPOXi0O1K9fn/HjxwMuzUSpDHFIIfKjOVSk9fSRRx7ppdjofUorns0nKj5UgVh50XFJ6WJqrmIYxoaDhf4NwzAMwzCMSFJQRVWoyOG0004DYOnSpQD0798/sorqnnvuCUDt2rWB8hVqbLfddpFLb/jjjz8Ap+hcdtllQCIEOXXq1HK/r8zzpfA8++yzFRlmQZCtmop0fvzxR8aNG1fIIVUIGairoYFQuHjWrFl5H1N5GTNmDDvuuCMAV111FQAPPfRQIYcUONrvyopuKKx+3XXXeUqqWrF+9913IY4wezSORx99FICOHTvm/B6fffaZZ+sXR9XcMIzsMEXVMAzDMAzDiCSRUFSFFB6Zw0cZf/7eSSedlPN73HfffWy99daAyx2TUXuhUOvbxx9/HICLLroISBQrqBCjb9++QHatKLfbbjvAWQbJCumNN94IcNT5Yf/99wdcu9xPPvnEy6+OI6eccgoA++23H+DUdLWQjQMqoGrVqpVXgPTII48UckiB8uSTTwIJlfuMM84AMrcdVt6ncv0bN27s/d6ipi6vX78egLVr1+b8WrWdvvXWW2PZdtswjNwwRdUwDMMwDMOIJJFSVKUUSIXTyjlKKE/x2GOPBVxL0GzYfPPNAWdv1KpVK++xxx57DIjOnFVFq3zhm266yVONpSx26NABcG0Z1fxAbLrppl5eq3J59b6//vprmMMPBbW1LCpKdFqUJQ44Y/VJkyYBJb+LKKL8Y6F9OQ4m/zvvvDPg2r1++eWX9OvXD4AffvihUMMKnIULF3p/qy2o7LZ+/vnnlOfq96jmIz/99BPt2rXLxzDLjVxFDjnkEPbdd9+0z5FNmvLcv/jiC8CpsoZhbNiYomoYhmEYhmFEkoIqqjVq1ABg5MiRAJx//vkAzJ07F4C77767MAMrheOPPx6AqlWrAtCsWTMABg4cyOrVq0t97dFHHw1A69atvfukyl177bVBD7VCKF9x2LBhQCL/7aabbgJc9fHs2bMBWLx4MQCjRo0CEtXwkFB4lP/Yv39/wG3rOKI8Yt1Onz7dU3nUFviFF14ozOByQHmdUiUXLVoEQI8ePQo1pKyRmi3P1N122w2AcePGRcbMPkhee+01IPGb0vZSVGLEiBEAdO3aFXC5qWLkyJEsWLAgTyMtHzpWKEpjGIbhxxRVwzAMwzAMI5IUldappKioKLA2Jttss42XO6bq/iFDhgBw8cUXA85bU55/Qef5FRcXFyX/X575qd2kci9VAZ4LUhEefvhhhg8fDsCKFStyfh8/QcwvE5UqVWKvvfYC4MEHHwRgjz32AEp2l5HqVVxc7HUHuuKKKwAqVCUf5vyyoX379oBzRHj55Ze9vFV1q6qIqhf2/OTZq313yy23BGDKlCkAtG3bNsiPK0EQ85MLhaItOqbsuuuuXketQhHm9jv44IO9SJNcUVauXAm47VipUkJ3UJTmrLPOqlBnKz+F/v2FjX9+hmFEA1NUDcMwDMMwjEiSN0V18uTJXj5Z9erVAahbty7gFDpVtYZFkIpA586dgUS1KkC1atXYYYcdgIT6Aa7SXX6jkydPBuDee+8FXDVrUORb8ZCf6q677grgKa4NGzYE4Omnn2bgwIFAogK5ohRa0dlqq60Ap0juueeenp9uEG4NYc9PbhP6vakvuvKs33zzzSA/rgSF3n5hE/b81HFL7gZ16tRJeVxd/JS7KsU1KDa27WcYRjTI24XqCSec4FnJLF++HHBtR6+88sqgPqZUNrYDrc0vXoQdOtYF9hZbbAG4350KwcLGtl+82djmZxhGNLDQv2EYhmEYhhFJ8qaoRoGNTRGw+cWLMOf3+eefe4VsgwcPBmDChAlBvX1W2PaLNxvb/AzDiAamqBqGYRiGYRiRxBTVDQibX7yx+cUbm1+8MUXVMKKJKaqGYRiGYRhGJClVUTUMwzAMwzCMQmGKqmEYhmEYhhFJ7ELVMAzDMAzDiCR2oWoYhmEYhmFEErtQNQzDMAzDMCKJXagahmEYhmEYkcQuVA3DMAzDMIxI8v8Axg/T/0fSQ6QAAAAASUVORK5CYII=\n","text/plain":["<Figure size 864x864 with 128 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"tOdQ3r5diEwr"},"source":["# Fully Connected VAE\n","\n","Our first VAE implementation will consist solely of fully connected layers. We'll take the `1 x 28 x 28` shape of our input and flatten the features to create an input dimension size of 784. In this section you'll define the Encoder and Decoder models in the VAE class of `vae.py` and implement the reparametrization trick, forward pass, and loss function to train your first VAE."]},{"cell_type":"markdown","metadata":{"id":"aqMjCTSMi0jX"},"source":["## FC-VAE Encoder\n","\n","Now lets start building our fully-connected VAE network. We'll start with the encoder, which will take our images as input (after flattening C,H,W to D shape) and pass them through a three Linear+ReLU layers. We'll use this hidden dimension representation to predict both the posterior mu and posterior log-variance using two separate linear layers (both shape (N,Z)). \n","\n","Note that we are calling this the 'logvar' layer because we'll use the log-variance (instead of variance or standard deviation) to stabilize training. This will specifically matter more when you compute reparametrization and the loss function later. \n","\n","*Define an `encoder`, `hidden_dim` (H), `mu_layer`, and `logvar_layer` in the initialization of the VAE class in `vae.py`. Use nn.Sequential to define the encoder, and separate Linear layers for the mu and logvar layers. In all of these layers, H will be a hidden dimension you set and will be the same across all encoder and decoder layers. Architecture for the encoder is described below:*\n","\n","\n"," * `Flatten` (Hint: nn.Flatten)\n"," * Fully connected layer with input size 784 (`input_size`) and output size H\n"," * `ReLU`\n"," * Fully connected layer with input_size H and output size H\n"," * `ReLU`\n"," * Fully connected layer with input_size H and output size H\n"," * `ReLU`\n"]},{"cell_type":"markdown","metadata":{"id":"gTuEAFrgkTyt"},"source":["## FC-VAE Decoder\n","\n","We'll now define the decoder, which will take the latent space representation and generate a reconstructed image. The architecture is as follows: \n","\n"," * Fully connected layer with input size as the latent size (Z) and output size H\n"," * `ReLU`\n"," * Fully connected layer with input_size H and output size H\n"," * `ReLU`\n"," * Fully connected layer with input_size H and output size H\n"," * `ReLU`\n"," * Fully connected layer with input_size H and output size 784 (`input_size`)\n"," * `Sigmoid`\n"," * `Unflatten` (nn.Unflatten)\n","\n","*Define a `decoder` in the initialization of the VAE class in `vae.py`. Like the encoding step, use `nn.Sequential`*  \n","\n"]},{"cell_type":"markdown","metadata":{"id":"aFTb-35TiOZl"},"source":["## Reparametrization \n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"SdD23s3Vf70-"},"source":["Now we'll apply a reparametrization trick in order to estimate the posterior $z$ during our forward pass, given the $\\mu$ and $\\sigma^2$ estimated by the encoder. A simple way to do this could be to simply generate a normal distribution centered at our  $\\mu$ and having a std corresponding to our $\\sigma^2$. However, we would have to backpropogate through this random sampling that is not differentiable. Instead, we sample initial random data $\\epsilon$ from a fixed distrubtion, and compute $z$ as a function of ($\\epsilon$, $\\sigma^2$, $\\mu$). Specifically:\n","\n","$z = \\mu + \\sigma\\epsilon$\n","\n","We can easily find the partial derivatives w.r.t $\\mu$ and $\\sigma^2$ and backpropagate through $z$. If $\\epsilon = \\mathcal{N} (0,1)$, then its easy to verify that the result of our forward pass calculation will be a distribution centered at $\\mu$ with variance $\\sigma^2$.\n","\n","Implement `reparametrization` in `vae.py` and verify your mean and std error are at or less than `1e-4`."]},{"cell_type":"code","metadata":{"id":"T236XnbhbVH4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1611295374469,"user_tz":480,"elapsed":311,"user":{"displayName":"김진혁","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh-KNOJTlJhKZYC2IjbUIPFTFKWoCXbFj273Tuhrg=s64","userId":"18139102342976554490"}},"outputId":"fead74be-1e62-4128-fb81-ce1e482a8123"},"source":["reset_seed(0)\n","from vae import reparametrize\n","latent_size = 15\n","size = (1, latent_size)\n","mu = torch.zeros(size)\n","logvar = torch.ones(size)\n","\n","z = reparametrize(mu, logvar)\n","\n","expected_mean = torch.FloatTensor([-0.4363])\n","expected_std = torch.FloatTensor([1.6860])\n","z_mean = torch.mean(z, dim=-1)\n","z_std = torch.std(z, dim=-1)\n","assert z.size() == size\n","\n","print('Mean Error', rel_error(z_mean, expected_mean))\n","print('Std Error', rel_error(z_std, expected_std))"],"execution_count":13,"outputs":[{"output_type":"stream","text":["Mean Error 5.639056398351415e-05\n","Std Error 7.1412955526273885e-06\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"XOfC7oDrkUhl"},"source":["## FC-VAE Forward\n","\n","Complete the VAE class by writing the forward pass. The forward pass should pass the input image through the encoder to calculate the estimation of mu and logvar, reparametrize to estimate the latent space z, and finally pass z into the decoder to generate an image.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"IQ0UXWyMi9Gu"},"source":["## Loss Function\n","\n","Before we're able to train our final model, we'll need to define our loss function. As seen below, the loss function for VAEs contains two terms: A reconstruction loss term (left) and KL divergence term (right). \n","\n","$-E_{Z~q_{\\phi}(z|x)}[log p_{\\theta}(x|z)] + D_{KL}(q_{\\phi}(z|x), p(z)))$\n","\n","Note that this is the negative of the variational lowerbound shown in lecture--this ensures that when we are minimizing this loss term, we're maximizing the variational lowerbound. The reconstruction loss term can be computed by simply using the binary cross entropy loss between the original input pixels and the output pixels of our decoder (Hint: `nn.functional.binary_cross_entropy`). The KL divergence term works to force the latent space distribution to be close to a prior distribution (we're using a standard normal gaussian as our prior).\n","\n","To help you out, we've derived an unvectorized form of the KL divergence term for you.\n","Suppose that $q_\\phi(z|x)$ is a $Z$-dimensional diagonal Gaussian with mean $\\mu_{z|x}$ of shape $(Z,)$ and standard deviation $\\sigma_{z|x}$ of shape $(Z,)$, and that $p(z)$ is a $Z$-dimensional Gaussian with zero mean and unit variance. Then we can write the KL divergence term as:\n","\n","$D_{KL}(q_{\\phi}(z|x), p(z))) = -\\frac{1}{2} \\sum_{j=1}^{J} (1 + log(\\sigma_{z|x}^2)_{j} - (\\mu_{z|x})^2_{j} - (\\sigma_{z|x})^2_{j}$)\n","\n","It's up to you to implement a vectorized version of this loss that also operates on minibatches.\n","You should average the loss across samples in the minibatch.\n","\n","Implement `loss_function` in `vae.py` and verify your implementation below. Your relative error should be less than or equal to `1e-5`\n","\n"]},{"cell_type":"code","metadata":{"id":"vF2ZUj2FjrFa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1611296012738,"user_tz":480,"elapsed":299,"user":{"displayName":"김진혁","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh-KNOJTlJhKZYC2IjbUIPFTFKWoCXbFj273Tuhrg=s64","userId":"18139102342976554490"}},"outputId":"00d9db11-acc1-4d28-c2c8-91e460de03da"},"source":["from vae import loss_function\n","size = (1,15)\n","\n","image = torch.sigmoid(torch.FloatTensor([[2,5], [6,7]]).unsqueeze(0).unsqueeze(0))\n","image_hat = torch.sigmoid(torch.FloatTensor([[1,10], [9,3]]).unsqueeze(0).unsqueeze(0))\n","\n","expected_out = torch.tensor(8.5079)\n","mu, logvar = torch.ones(size), torch.zeros(size)\n","out = loss_function(image, image_hat, mu, logvar)\n","print('Loss error', rel_error(expected_out,out))\n"],"execution_count":22,"outputs":[{"output_type":"stream","text":["Loss error 2.1297676389877955e-06\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"wV8fbzenkAXm"},"source":["\n","## Train a model\n","\n","Now that we have our VAE defined and loss function ready, lets train our model! Our training script is provided  in `a6_helper.py`, and we have pre-defined an Adam optimizer, learning rate, and # of epochs for you to use. \n","\n","Training for 10 epochs should take ~2 minutes and your loss should be less than 120."]},{"cell_type":"code","metadata":{"id":"rWaaacNHsfao","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1611296667894,"user_tz":480,"elapsed":67275,"user":{"displayName":"김진혁","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh-KNOJTlJhKZYC2IjbUIPFTFKWoCXbFj273Tuhrg=s64","userId":"18139102342976554490"}},"outputId":"ca125aa9-e78c-4feb-f4d5-ad3bdac14286"},"source":["num_epochs = 10\n","latent_size = 15\n","from vae import VAE\n","from a6_helper import train_vae\n","input_size = 28*28\n","device = 'cuda'\n","vae_model = VAE(input_size, latent_size=latent_size)\n","vae_model.cuda()\n","for epoch in range(0, num_epochs):\n","  train_vae(epoch, vae_model, loader_train)\n"],"execution_count":36,"outputs":[{"output_type":"stream","text":["Train Epoch: 0 \tLoss: 155.084198\n","Train Epoch: 1 \tLoss: 135.607468\n","Train Epoch: 2 \tLoss: 128.389252\n","Train Epoch: 3 \tLoss: 125.618248\n","Train Epoch: 4 \tLoss: 122.922928\n","Train Epoch: 5 \tLoss: 116.850220\n","Train Epoch: 6 \tLoss: 115.151512\n","Train Epoch: 7 \tLoss: 113.835854\n","Train Epoch: 8 \tLoss: 110.008133\n","Train Epoch: 9 \tLoss: 116.909279\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"JT6Ek-26jjJD"},"source":["## Visualize results\n","\n","After training our VAE network, we're able to take advantage of its power to generate new training examples. This process simply involves the decoder: we intialize some random distribution for our latent spaces z, and generate new examples by passing these latent space into the decoder. \n","\n","Run the cell below to generate new images! You should be able to visually recognize many of the digits, although some may be a bit blurry or badly formed. Our next model will see improvement in these results. "]},{"cell_type":"code","metadata":{"id":"RhhrsgrMTyTi","colab":{"base_uri":"https://localhost:8080/","height":84},"executionInfo":{"status":"ok","timestamp":1611296752654,"user_tz":480,"elapsed":800,"user":{"displayName":"김진혁","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh-KNOJTlJhKZYC2IjbUIPFTFKWoCXbFj273Tuhrg=s64","userId":"18139102342976554490"}},"outputId":"0a60de34-995f-4607-eb19-337213b4245a"},"source":["z = torch.randn(10, latent_size).to(device='cuda')\n","import matplotlib.gridspec as gridspec\n","vae_model.eval()\n","samples = vae_model.decoder(z).data.cpu().numpy()\n","\n","fig = plt.figure(figsize=(10, 1))\n","gspec = gridspec.GridSpec(1, 10)\n","gspec.update(wspace=0.05, hspace=0.05)\n","for i, sample in enumerate(samples):\n","  ax = plt.subplot(gspec[i])\n","  plt.axis('off')\n","  ax.set_xticklabels([])\n","  ax.set_yticklabels([])\n","  ax.set_aspect('equal')\n","  plt.imshow(sample.reshape(28,28), cmap='Greys_r')\n","  plt.savefig(os.path.join(GOOGLE_DRIVE_PATH,'vae_generation.jpg'))"],"execution_count":37,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x72 with 10 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"sx3HGSpXk1MY"},"source":["## Latent Space Interpolation\n","\n","As a final visual test of our trained VAE model, we can perform interpolation in latent space. We generate random latent vectors $z_0$ and $z_1$, and linearly interplate between them; we run each interpolated vector through the trained generator to produce an image.\n","\n","Each row of the figure below interpolates between two random vectors. For the most part the model should exhibit smooth transitions along each row, demonstrating that the model has learned something nontrivial about the underlying spatial structure of the digits it is modeling."]},{"cell_type":"code","metadata":{"id":"XZ_4XsFURmN1","colab":{"base_uri":"https://localhost:8080/","height":660},"executionInfo":{"status":"ok","timestamp":1611296774010,"user_tz":480,"elapsed":4420,"user":{"displayName":"김진혁","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh-KNOJTlJhKZYC2IjbUIPFTFKWoCXbFj273Tuhrg=s64","userId":"18139102342976554490"}},"outputId":"ee4adde2-7ffc-4aa7-ef2c-a34de32f4283"},"source":["S = 12\n","latent_size = 15\n","device = 'cuda'\n","z0 = torch.randn(S,latent_size , device=device)\n","z1 = torch.randn(S, latent_size, device=device)\n","w = torch.linspace(0, 1, S, device=device).view(S, 1, 1)\n","z = (w * z0 + (1 - w) * z1).transpose(0, 1).reshape(S * S, latent_size)\n","x = vae_model.decoder(z)\n","show_images(x.data.cpu())"],"execution_count":38,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x864 with 144 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"wzS_ufzEkhah"},"source":["# Conditional FC-VAE \n","\n","The second model you'll develop will be very similar to the FC-VAE, but with a slight conditional twist to it. We'll use what we know about the labels of each MNIST image, and *condition* our latent space and image generation on the specific class. Instead of $q_{\\phi} (z|x)$ and $p_{\\phi}(x|z)$ we have $q_{\\phi} (z|x,c)$  and $p_{\\phi}(x|z, c)$\n","\n","This will allow us to do some powerful conditional generation at inference time. We can specifically choose to generate more 1s, 2s, 9s, etc. instead of simply generating new digits randomly."]},{"cell_type":"markdown","metadata":{"id":"hle0JuhwklKc"},"source":["## Define Network with class input\n","\n","Our CVAE architecture will be the same as our FC-VAE architecture, except we'll now add a one-hot label vector to both the x input (in our case, the flattened image dimensions) and the z latent space. \n","\n","If our one-hot vector is called `c`, then `c[label] = 1` and `c = 0` elsewhere.\n","\n","For the `CVAE` class in `vae.py` use the same FC-VAE architecture implemented in the last network with the following modifications:\n","\n","1. Modify the first linear layer of your `encoder` to take in not only the flattened input image, but also the one-hot label vector `c`\n","2. Modify the first layer of your `decoder` to project the latent space + one-hot vector to the `hidden_dim`\n","3. Lastly, implement the `forward` pass to combine the flattened input image with the one-hot vectors (`torch.cat`) before passing them to the `encoder` and combine the latent space with the one-hot vectors (`torch.cat`) before passing them to the `decoder`"]},{"cell_type":"markdown","metadata":{"id":"bUzKyFI9kp8i"},"source":["## Train model\n","\n","Using the same training script, let's now train our CVAE! \n","\n","Training for 10 epochs should take ~2 minutes and your loss should be less than 120."]},{"cell_type":"code","metadata":{"id":"N1dzKDUsunbD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1611297412500,"user_tz":480,"elapsed":71987,"user":{"displayName":"김진혁","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh-KNOJTlJhKZYC2IjbUIPFTFKWoCXbFj273Tuhrg=s64","userId":"18139102342976554490"}},"outputId":"8fad54d7-f191-450a-ad26-a206d44c61a7"},"source":["from vae import CVAE\n","num_epochs = 10\n","latent_size = 15\n","from a6_helper import train_vae\n","input_size = 28*28\n","device = 'cuda'\n","\n","cvae = CVAE(input_size, latent_size=latent_size)\n","cvae.cuda()\n","for epoch in range(0, num_epochs):\n","  train_vae(epoch, cvae, loader_train, cond=True)"],"execution_count":48,"outputs":[{"output_type":"stream","text":["Train Epoch: 0 \tLoss: 138.056534\n","Train Epoch: 1 \tLoss: 120.925568\n","Train Epoch: 2 \tLoss: 118.814415\n","Train Epoch: 3 \tLoss: 115.436127\n","Train Epoch: 4 \tLoss: 109.227959\n","Train Epoch: 5 \tLoss: 105.887589\n","Train Epoch: 6 \tLoss: 110.695755\n","Train Epoch: 7 \tLoss: 107.580574\n","Train Epoch: 8 \tLoss: 100.823265\n","Train Epoch: 9 \tLoss: 102.213303\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"GMAyFBZTkr1Y"},"source":["## Visualize Results\n","\n","We've trained our CVAE, now lets conditionally generate some new data! This time, we can specify the class we want to generate by adding our one hot matrix of class labels. We use `torch.eye` to create an identity matrix, gives effectively gives us one label for each digit. When you run the cell below, you should get one example per digit. Each digit should be reasonably distinguishable (it is ok to run this cell a few times to save your best results).\n","\n"]},{"cell_type":"code","metadata":{"id":"GCfwpz0NALdZ","colab":{"base_uri":"https://localhost:8080/","height":84},"executionInfo":{"status":"ok","timestamp":1611297419016,"user_tz":480,"elapsed":844,"user":{"displayName":"김진혁","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh-KNOJTlJhKZYC2IjbUIPFTFKWoCXbFj273Tuhrg=s64","userId":"18139102342976554490"}},"outputId":"beb654f2-a401-4467-a199-92af18bb6852"},"source":["z = torch.randn(10, latent_size)\n","c = torch.eye(10, 10) # [one hot labels for 0-9]\n","import matplotlib.gridspec as gridspec\n","z = torch.cat((z,c), dim=-1).to(device='cuda')\n","cvae.eval()\n","samples = cvae.decoder(z).data.cpu().numpy()\n","\n","fig = plt.figure(figsize=(10, 1))\n","gspec = gridspec.GridSpec(1, 10)\n","gspec.update(wspace=0.05, hspace=0.05)\n","for i, sample in enumerate(samples):\n","  ax = plt.subplot(gspec[i])\n","  plt.axis('off')\n","  ax.set_xticklabels([])\n","  ax.set_yticklabels([])\n","  ax.set_aspect('equal')\n","  plt.imshow(sample.reshape(28, 28), cmap='Greys_r')\n","  plt.savefig(os.path.join(GOOGLE_DRIVE_PATH,'conditional_vae_generation.jpg'))"],"execution_count":49,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x72 with 10 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"vW8GmNSwY5Jx"},"source":["## Final Check\n","\n","Make sure all your training results (loss + images) are saved in the notebook. You can run \"Runtime -> Restart and run all...\" to double check before submitting"]}]}